Zuo Gui Wan Restores Bone Metabolism in Postmenopausal Osteoporosis through HIF-1 and PI3K–Akt Pathway Modulation: Evidence from UPLC– MS/MS and Network Analysis

Zuo Gui Wan Restores Bone Metabolism in Postmenopausal Osteoporosis through HIF-1 and PI3K–Akt Pathway Modulation: Evidence from UPLC– MS/MS and Network Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Zuo Gui Wan Restores Bone Metabolism in Postmenopausal Osteoporosis through HIF-1 and PI3K–Akt Pathway Modulation: Evidence from UPLC– MS/MS and Network Analysis Jinguang Gu, Weikai Qin, Peng Feng, Chenhua Li, Baoyu Qi, Bing Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8011125/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Ethnopharmacological Relevance: Zuo Gui Wan (ZGW), a traditional Chinese medicine (TCM) formula, shows potential for treating postmenopausal osteoporosis (PMOP), combining traditional herbal knowledge with modern scientific validation. Background: Postmenopausal osteoporosis (PMOP) is a metabolic bone disorder caused by estrogen deficiency, leading to decreased bone mass and increased fracture risk. ZGW has shown promise in managing PMOP, but its active metabolites and mechanisms remain unclear. Methods: Ovariectomized (OVX) rats were used to model osteoporosis. ZGW's efficacy was evaluated through micro-CT, HE staining, and serum ELISA. Active metabolites in serum were identified by UPLC-MS/MS. A "botanical drug-metabolite-target-disease" network was built using network analysis. Pathway enrichment was performed using GO and KEGG in R. Molecular docking of key metabolites and targets was conducted using AutoDock Vina and PyMOL. In vitro assays, including MTT, ALP, Alizarin Red S staining, PCR, and Western blotting, validated osteogenic effects. Results: ZGW improved bone microstructure and serum bone metabolism in OVX rats. UPLC-MS/MS identified 209 metabolites, with 20 transferring into the serum. PPI analysis revealed 144 key targets, and molecular docking showed strong binding between active metabolites (e.g., Remycin A, Farnesecin) and their targets, such as ALB and EGFR. GO and KEGG analyses identified pathways like HIF-1, estrogen signaling, and PI3K-Akt. In vitro, ZGW activated these pathways, enhancing osteogenic marker expression and promoting osteoblast proliferation and differentiation. Conclusion: ZGW treats PMOP through multiple mechanisms involving active metabolites, targets, and pathways. It restores normal gene expression and modulates pathways such as HIF-1 and PI3K-Akt, while also inhibiting inflammation. This study highlights the power of combining UPLC-MS/MS with network analysis for exploring TCM formulations in PMOP treatment. Network analysis UPLC-MS/MS Zuo Gui Wan Postmenopausal Osteoporosis Osteoporosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1 Introduction PMOP represents a significant public health issue, characterized by a systemic decline in bone metabolism due to reduced estrogen levels[ 1] , leading to decreased bone mass and deteriorated trabecular architecture. This results in bones that are fragile and susceptible to fractures[ 2] . It is estimated that 35.3% of elderly women worldwide are affected by PMOP[ 3] , with substantial incidence rates in the UK where half of the women sustain at least one fragility fracture, as opposed to a fifth of men[ 4] . Given these statistics, effective treatment options are essential not only for enhancing the quality of life of patients but also for lessening societal healthcare burdens. Historically, hormone replacement therapy (HRT) and bisphosphonates have been the mainstays of treatment, yet they are not devoid of drawbacks. For instance, extended administration of HRT can result in negative outcomes such as breast cancer and cardiovascular conditions, whereas bisphosphonates are associated with serious complications like osteonecrosis of the jaw[ 5] . Consequently, there is an increasing demand for safe and effective alternative therapies. Traditional Chinese medicine (TCM) has been employed for centuries to manage bone ailments. ZGW, originating from “Jingyue Quanshu·New Prescriptions Eight Arrays” by Zhangjiebin, comprises eight botanical drugs: Cuscuta chinensis Lam. (Tu Sizi), Dioscorea oppositifolia L. (Shan Yao), Lycium barbarum L. (Gou Qi), Cervi Cornus Colla (Lu Jiaojiao), Rehmannia glutinosa (Gaertn.) DC (Shu Di), Cornus officinalis Siebold & Zucc. (Shan Zhuyu), Testudinis Carapacis Et Plastri Colla (Gui Banjiao), and Cyathula officinalis K.C.Kuan (Niu Xi). Verified against the MPNS database, these metabolites are traditionally used to alleviate symptoms such as lumbar pain, leg weakness, and symptoms arising from yin deficiency. Specifically, Rehmanniae Radix Praeparata is noted for its antioxidative and endocrine regulatory functions; Dioscoreae Rhizoma is recognized for its anti-aging properties and immune-enhancing effects[ 6] ; polysaccharides in Lycii Fructus exhibit anti-inflammatory and anti-apoptotic properties[ 7] ; and Corni Fructus influences bone metabolism-related signaling pathways[ 8] . Collectively, these effects synergistically combat the negative impact of estrogen deficiency, inhibiting bone resorption and fostering bone formation, thereby aiding in the prevention and treatment of PMOP. Research indicates that ZGW impacts the progression of osteoporosis through the RANKL/OPG pathway[ 9] and helps restore bone mass lost due to diminished estrogen levels. However, despite its demonstrated efficacy, the clinical use of ZGW is mired in controversies. Firstly, the complexity of its herbal metabolites and the ambiguity of its active metabolites challenge its standardization. Secondly, the intricacies of its production process call for rigorous safety assessments. This study, therefore, integrates UPLC-MS/MS with network analysis and experimental validations to elucidate the effective metabolites and mechanisms by which ZGW combats PMOP and to confirm its safety(Fig.1), offering new theoretical and empirical support for the application of TCM in treating this condition. 2 Materials and methods 2.1 Materials and reagents Zuo Gui Wan:24g of Rehmanniae Radix Praeparata, 12g of Dioscoreae Rhizoma, Lycii Fructus, Corni Fructus, Cuscutae Semen, Cervi Cornus Colla, Testudinis Carapacis Et Plastri Colla, and 9g of Cyathulae Radixwere all provided by the pharmacy of Wangjing Hospital, China Academy of Chinese Medical Sciences. All samples were kept in the Medical Experimental Centre of China Academy of Traditional Chinese Medicine. The following experimental equipment was used: vertical electrophoresis (Servicebio, model BV-2); microscope (Nikon, model E100); enzyme labelling instrument (BioTeK, model Epoch); desktop high-speed refrigerated microcentrifuge (DragonLab, model D3024R); fluorescence quantitative PCR instrument (Bio-rad, CFX Connect model). The following experimental reagents were used: MTT assay kit (Wuhan Xavier Biotechnology Co., Ltd., No. G4101); BCA protein quantitative detection kit (Wuhan Xavier Biotechnology Co., Ltd., No. G2026-200T); alkaline phosphatase assay kit (Nanjing Jianjian Bioengineering Research Institute, No. A0592); α-MEM liquid culture medium ( HyClone, No. SH30265.01); Australian fetal bovine serum (Gibco, No. 10099-141); penicillin-streptomycin solution (HyClone, No. SV30010); 0.25% trypsin (Gibco, No. SH30042.01); osteogenic inducing agents (Elabscience Biotechnology Co. Ltd, No. PD-033); SweScript All-in-One RT SuperMix for qPCR (One-Step gDNA Remover) (Wuhan Xavier Biotechnology Co. Ltd, No. G3337); 2×Universal Blue SYBR Green qPCR Master Mix (Wuhan Xavier Biotechnology Co., Ltd., No. G3326). 2.2 Animals and Cells 62 female SPF-grade SD rats, aged 8 weeks and weighing approximately 200±20 grams, were obtained using an animal production license (SCXK (Beijing) 2019-0010) held by Sipf Bio-Tech Co., Ltd. These rats were housed at a facility in the Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences. The environmental conditions were controlled with a 12-hour light/dark cycle, humidity levels of 40% to 50%, and temperatures between 20°C and 25°C. MC3T3-E1 cells were sourced from Saibekang Biotechnology Co., Ltd. (Shanghai, China) (Catalog number iCell-m031). BMSCS cells were obtained from Haixing Biotechnology Co., Ltd. (Fujian, China) (Catalog number BMRS-C106I). The animal study described in this research was ethically approved by the Ethics Center of the Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences. (Approval No. 2025B036), in compliance with the guidelines outlined in the 1986 EEC Directive (86/609/EEC). 2.3 Preparation of Drug-Containing Serum The ZGW formula contains several herbal metabolites, including 24g of Rehmannia, 12g of Sanguisorba, 12g of Lycium, 12g of Cornus, 12g of Cuscuta, and 9g of Cornus. These botanical drugs were soaked for 1 hour, followed by decoction for 1 hour. Afterward, they were soaked for 40 minutes, and then decocted again for 1 hour to obtain the second batch. Both decoctions were combined and concentrated to form a Chinese medicine solution with a density of 1.1025g/ml. 20 SD rats were divided into two groups: a control group and a treatment group, with 10 rats in each group. Based on the human equivalent dose (calculated using a standard body weight of 60 kg), the treatment group received a ZGW solution at a concentration of 1.1025g/ml, administered once daily at a dose of 1 ml/ (100g·d) for 7 days. The control group was administered the same volume of saline solution. One hour after the final dose, the rats were sedated and blood samples were collected via the inferior vena cava. The blood was kept at 4°C for 3 hours, then centrifuged at 3000 rpm for 15 minutes (centrifugal radius of 14 cm) at 4°C. The serum was inactivated in a 56°C water bath for 30 minutes and stored at -80°C for subsequent analysis. 2.4 UPLC-MS/MS Analysis For the analysis of ZGW active metabolites, UPLC-MS/MS was employed to examine the prototype drug along with blank and medicated serum samples from rats. The chromatographic examination was carried out on 100 μL of each sample type using a Waters UPLC HSS T3 column (1.8 μm, 2.1 mm × 100 mm). The chromatographic separation was carried out using a gradient elution approach, as shown in Table 1. The mobile phase consisted of solvent B (methanol) and solvent A (water + 0.1% formic acid). The column temperature was kept at 40°C, the injection volume was 10.0 μl, and the flow rate was set at 0.3 ml/min. An electrospray ionization source was utilized in the mass spectrometry analyses performed utilizing a Q Exactive™ quadrupole-orbitrap ion trap mass spectrometer. The device was set up with a positive ion source voltage of 3.7 kV and a negative ion source voltage of 3.5 kV. With 30 psi for the sheath and 10 psi for the auxiliary gas, the capillary temperature was fixed at 320°C. With nitrogen acting as both a sheath and collision gas, the temperature of solvent evaporation was maintained at 300°C, with the latter operating at a pressure of 1.5 mTorr. A resolution of 70000, an automated gain control (AGC) target of 1×10 6 , a maximum ion isolation duration of 50 ms, and a mass-to-charge ratio scan range of 100-1500 were all part of the full scan parameters. Mass spectrometer calibration utilized external standards to maintain a mass error within 5 ppm, including calibration ions for both positive (74.09643, 83.06037, 195.08465, 262.63612, 524.26496, 1022.00341) and negative (91.00368, 96.96010, 112.98559, 265.14790, 514.28440, 1080.00999) modes. Using parameters like a resolution of 17500, an AGC goal of 1×10 5 , and a maximum ion isolation duration of 50 ms, metabolicate identification made use of the dd-MS2 scanning mode, which is data-dependent scanning mode. Up to 10 secondary ion fragments were examined per scan with a dynamic exclusion, mass separation window of 2, collision energy set at 30 V, and an intensity threshold of 1×10 5 . Data acquisition and system control were facilitated by Xcalibur software version 2.2 SP1.48. Table 1 Elution gradient time (min) mobile phase A （v%） B （v%） 0 2 98 1.0 2 98 41.0 100 0 50.0 100 100 50.1 2 98 52.0 2 98 2.5 In vivo experiment 2.5.1Animal Model and Grouping: To establish an ovariectomized rat model of osteoporosis, the experiment was conducted in accordance with the National Regulations for Animal Experimentation. The PMOP rat model induced by OVX was established following methods described in previous literature [ 10] . The procedure is as follows: After anesthetizing the rats with a 2% sodium pentobarbital solution (40 mg/kg), the rats were placed in a prone position, and a 2 cm long incision was made along the midline of the back. Muscle tissue was carefully separated to locate and sequentially excise the bilateral ovaries. The incision was then sutured layer by layer and disinfected with iodine tincture. After surgery, rats were intraperitoneally injected with penicillin sodium solution (80,000 units) for three consecutive days to prevent infection.A total of forty-two Sprague–Dawley (SD) rats were randomly divided into seven groups (n = 6 per group): normal control (NC), sham-operated (SHAM), ovariectomized model (OVX), low-dose Zuo Gui Wan (ZGW-L), medium-dose Zuo Gui Wan (ZGW-M), high-dose Zuo Gui Wan (ZGW-H), and positive control with alendronate sodium (ALN). Rats in the NC group were maintained under standard feeding conditions and received 0.9% saline by gavage. In the SHAM group, a subcutaneous incision was made and sutured without further procedures, followed by gavage with 0.9% saline. The OVX group underwent bilateral ovariectomy to induce osteoporosis. The ZGW-L, ZGW-M, and ZGW-H groups received ZGW extract at concentrations of 0.5513 g/mL, 1.1025 g/mL, and 2.205 g/mL, respectively, at a dose of 1 mL/100 g body weight once daily. The ALN group received alendronate sodium enteric-coated tablets at 6.3 mg/kg/week by gavage. All treatments lasted for 12 weeks. After the final dose, 24 hours later, the rats were anesthetized with intraperitoneal injection of sodium pentobarbital (3%, 0.15mL/100g). The left tibia was excised and soaked in 4% paraformaldehyde solution for hematoxylin-eosin (HE) staining. The right tibia was preserved at −80°C for Western blot analysis. The right femur was wrapped with moist gauze for biomechanical testing, and the left femur was immersed in 4% paraformaldehyde solution for micro-CT scanning. 2.5.2 Micro-Computed Tomography (Micro-CT): Micro-CT scanning of the femur was performed using a Skyscan 1276 micro-CT (Bruker, USA), with a voltage of 70 kV, current of 200 μA, and a scanning resolution of 10.2 μm, with a field of view of 2016×1344. The bone marrow cavity of the distal femur, 3mm below the growth plate, was defined as the region of interest (ROI), where trabecular bone morphology parameters and bone mineral density (BMD) were measured. The primary analysis indicators included: bone mineral density (BMD) (g/cm³), trabecular thickness (Tb.Th) (mm), bone volume fraction (BV/TV) (%), trabecular number (Tb.N) (1/mm), trabecular separation (Tb.Sp) (mm), and Structure Model Index (SMI). 2.5.3 H&E Staining of Tibial Bone Tissue: The proximal tibial tissue was decalcified in 10% EDTA solution for 1 month, with the solution being changed weekly. The hardness of the tissue was monitored throughout the decalcification process. After decalcification, femur sections (5 μm thickness) were dewaxed and rehydrated, then stained with hematoxylin and eosin for 5 minutes, followed by dehydration, clearing, and mounting. All sections were examined under a microscope to record the effects of staining on tissue structure. 2.5.4 Enzyme-Linked Immunosorbent Assay (ELISA) Detection: Serum samples from rats were used to detect the levels of estradiol (E2), bone-specific alkaline phosphatase (BALP), type I collagen C-terminal cross-linking telopeptide (CTX-1), and osteocalcin (BGP) using commercial ELISA kits according to the manufacturer’s instructions. The ELISA kits were purchased from Enzyme-linked Biotech Co., Ltd. (Shanghai, China). 2.6 network analysis Analysis 2.6.1 Identification of ZGW metabolites and Action Target Prediction By utilizing ultra-performance liquid chromatography linked to a quadrupole orbitrap mass spectrometer (UPLC-Q-Orbitrap-MS), the metabolites of the TCM formulation ZGW were described. This process included the analysis of both in vitro chemical metabolites and in vivo metabolic products. Information regarding individual herbal metabolites was collated from various commercial and public databases such as UNIFI, HERB, TCMSP, and ETCM, supplemented by existing scientific literature. After compiling this data and removing duplicates, a comprehensive database of single-herb chemical metabolites specific to ZGW was created. Crucial metabolites were pinpointed based on the Chinese Pharmacopoeia and pertinent analytical literature. A multi-dimensional analysis was carried out considering factors like parent ion mass accuracy, match of secondary fragments, isotopic distribution, and peak intensities to confirm the identified metabolites. These metabolites were then validated, and their Isomeric and Canonical SMILES were retrieved from PubChem. Prediction of the biological targets for these metabolites was performed using tools like Swiss Target Prediction and Super-PRED, retaining targets with probabilities greater than zero in Swiss Target Prediction and 60% or higher in Super-PRED, followed by deduplication. 2.6.2 Collection of PMOP Targets Targets associated with PMOP were sourced from several prominent databases, including DrugBank[ 11] , GeneCards[ 12] , TTD[ 13] , DisGeNET[ 14] , and OMIM[ 15] . These resources provided a comprehensive list of potential PMOP-related targets. The data from these platforms were integrated and duplicates were removed. Additionally, the most significant 1000 targets were identified from the Gene Expression Omnibus (GEO) using an analysis of GSE230665.top.table (1) with geo2r, focusing on those with the lowest p-values. 2.6.3 Construction of the "botanical drug-metabolite-Target-Disease" Network We used Cytoscape 3.8.0 to build the "botanical drug-metabolite-target-disease" network, which combines the anticipated targets from ZGW with the gathered PMOP-related targets. Within this network, active metabolites were identified based on their Node Betweenness, utilizing the CytoNCA plugin, highlighting those with significant influence across the network. 2.6.4 PPI Analysis of Intersecting Targets Intersecting targets from ZGW and PMOP were analyzed through the STRING database, selecting interactions with a confidence score above 0.4. The resulting interaction data were exported as a TSV file. Subsequently, details regarding node1, node2, and the combined score were imported into Cytoscape to establish the PPI network. Hub genes within this network were determined using the CytoNCA tool. Additionally, Sankey diagrams depicting the relationships between drugs, core metabolites, and key genes were created. 2.6.5 Enrichment Analysis Enrichment analyses for GO and KEGG pathways of the intersecting targets were conducted utilizing the “clusterProfiler [4.4.4]” package. Results from these analyses were visualized with the “ggplot2 [3.3.6]” package, and corresponding bubble charts were generated. 2.6.6 Molecular Docking After retrieving the three-dimensional (3D) structural data of important metabolites from PubChem in SDF format, we used Open Babel to convert them to PDB format. In order to create clean PDB files, the 3D crystal structures of key genes were obtained from the Protein Data Bank (PDB) and, using PyMol 2.4.0, any unnecessary ions or water molecules were eliminated. The next step in identifying active binding sites was to convert these metabolites and target proteins to PDBQT format. To visualize the docking data, we used PyMol 2.4.0 and ran the molecular docking simulations with Autodock Vina. 2.7 Experimental programme 2.7.1 Cell Culture and Treatment: MC3T3-E1 cells were cultured in α-MEM base medium, while BMSCS cells were cultured in DMEM base medium. The efficacy experiments included a blank serum group and a medicated serum group, with different serum concentrations used for interventions. In the network analysis mechanism verification, in addition to the blank group (NC) and the Zuo Gui Wan (ZGW) medicated serum group, two additional groups were included: the MC3T3-E1 medicated group with the addition of LY294002 (PI3K/AKT inhibitor) (ZGW+LY), and the MC3T3-E1 medicated group with both LY294002 and DMOG (HIF pathway activator) (ZGW+LY+DMOG). 2.7.2 Safety and Cell Proliferation Assay of ZGW P3 MC3T3-E1 cells were plated in 96-well plates at 1×105 cells/ml, 100 μl per well. After incubating for 24 hours, the cells were organized into various groups receiving either control serum or drug-containing serum at concentrations of 10%, 15%, 20%, and 25%. Three times were each treatment condition repeated. Additional 24, 48, and 72 hour incubation durations were subsequently applied to the cells. Then, 50 μL of 1× MTT reagent was added to every well and left to incubate for four hours. Each well was then treated with 150 μL of DMSO after the culture media was removed, and the mixture was incubated for 10 minutes with gentle shaking. To find out how viable each group's cells were, a microplate reader measured their optical density (OD) at 570 nm. 2.7.3 Detection of cellular alkaline phosphatase activity The cellular alkaline phosphatase activity was detected by microplate assay, according to the cell concentration of 1×105/mL, the 3rd generation MC3T3-E1 cells were inoculated in 96-well plates, each well was cultured with 100 μL α-MEM complete medium, and after 24 h of inoculation, the cells were divided into 20%, control serum group, and the corresponding volume fraction of the drug-containing serum group, and corresponding serum interventions were carried out respectively, and each group was set up with three metabolite wells, incubated in the incubator for 48 h, the upper layer of the cell culture medium was taken, and the alkaline phosphatase activity in the culture medium was detected according to the instructions of the kit. 2.7.4 Detection of osteogenic activity Alizarin red S staining was used to detect the osteogenic activity of the cells, and the 3rd generation MC3T3-E1 cells were inoculated into 24-well plates according to the cell concentration of 1×105/mL, and each well was cultured with 400 μL of α-MEM complete medium, and after 24 h of inoculation, the cells were divided into two groups: a 20% control serum group and a 20% drug-containing serum group. Each group was treated with the corresponding serum. Additionally, an equal volume of 1% osteogenic differentiation induction medium was added to each group. Alizarin Red S staining was performed on day 20. Firstly, the cell culture solution of each group was thoroughly aspirated, and the cells were washed twice with PBS, 400 µL of 40 g/L neutral formaldehyde solution was added to each well, and the formaldehyde solution was aspirated after fixation at room temperature for 15 min, the cells were washed twice with PBS, 400 µL of alizarin red S staining solution was added along the edges of the wells, and the cells were incubated at room temperature and protected from light for 15 min, then the alizarin red S staining solution was aspirated and washed three times with PBS in order to reduce the background staining, and finally the calcification of each group of cells was observed by microscope, and the staining results were photographed and recorded. 2.7.5 Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) The Trizol reagent was used to extract total RNA from every single cell type. One way to measure the concentration and purity of RNA is with a micro-spectrophotometer. After these measurements, a 20μL reaction volume of mRNA was transformed into cDNA using a Takara reverse transcription kit. Table 2 details the primer sequences and PCR conditions. The following settings were used to run the qRT-PCR: denaturation at 95°C for 30 seconds for one cycle, PCR amplification at 95°C for 5 s and 60°C for 30 s for 40 cycles, and finally, a melting curve analysis with 30 s at 60°C, 1 minute at 95°C, and 15 s at 95°C. The analytic program was used to extract data, utilizing GAPDH as the reference gene. Triplicates of each experimental condition were conducted. We used the 2 −ΔΔCT method to quantify gene expression, and then we used statistical analysis and bar graphs to show the results. Table 2. Sequences of PCR primers Gene symbol Accession number Forward primer (5’-3’) Reverse primer (5’-3’） Amplicon size MMP9 NM_013599.4 CTCGGGAAGGCTCTGCTGTT AACTCACACGCCAGAAGAATTTG 190 ESR1 NM_001302531.1 CAGGCTTTGGGGACTTGAAT GAGCAAGTTAGGAGCAAACAGGA 134 ALB NM_009654.4 CGCTACACCCAGAAAGCACCT ACGGTTCAGGATTGCAGACAGATA 150 EGFR NM_007912.4 CCGAAACTACGTGGTGACAGAT TGCCATTACAAACTTTGCGAC 131 NFKB1 NM_001410442.1 GAGTCACGAAATCCAACGCAG CGTCATCACTCTTGGCACAATC 90 TLR4 NM_021297.2 TGAGGACTGGGTGAGAAATGAGC CTGCCATGTTTGAGCAATCTCAT 223 STAT3 NM_011486.5 TGCGGAGAAGCATTGTGAGTG TCTTAATTTGTTGGCGGGTCT 210 HIF1A NM_001313919.1 TTGCTTTGATGTGGATAGCGATA CATACTTGGAGGGCTTGGAGAAT 223 MAPK1 NM_053842.2 AACCTCCTGCTGAACACCACT CGTGGCTACATACTCTGTCAAGAAC 111 PIK3CD NM_001029837.2 TCCTTCGCCATCAAGTCCCT AGAGCGGAGGTGCCAGAACA 180 PIK3R1 NM_001024955.2 TTGACAGTAGGAGGAGGTTGGA CAGGGAGTATTGATCTTCGGTATT 212 PIK3CB NM_029094.3 GTGCTAATGTGTCAAGTCGTGGTG CAGTCTTGCCGTAGAGTCCAAATAA 127 NOS3 NM_008713.4 CAATCTTCGTTCAGCCATCACAG GGAGCCATCCTGCTGCCTAT 110 PIK3CA NM_008839.3 ATGGAGGAGAACCCTTATGTGAC AGATTGAAAGGCAAAGGCGC 135 SERPINE1 NM_008871.2 GGCCTCCAAAGACCGGAAT ACAAAGATGGCATCCGCAGTA 222 MTOR NM_020009.2 CCTTCACAGATACCCAGTACCTCC AGTAGACCTTAAACTCCGACCTCAC 138 OSTERIX NM_001348205.1 TCTGCGGCAAGAGGTTCACT GCTGATGTTTGCTCAAGTGGTC 132 RUNX2 NM_001145920.2 ATGACACTGCCACCTCTGACTTCT AGGGATGAAATGCTTGGGAACT 121 GAPDH NM_008084.2 CCTCGTCCCGTAGACAAAATG TGAGGTCAATGAAGGGGTCGT 133 OPG NM_012870.2 AATTGTGGAATAGATGTCACCCTGT CAAACTGTCCACCAGAACACTCA 102 2.7.6 Western Blot Protein immunoblotting was used to detect the detection of protein expression of MC3T3-E1 cells in each group. According to the cell concentration of 1×105/mL, the 3rd generation MC3T3-E1 cells were inoculated in 10 cm dishes with 10 ml each and incubated for 24 h. 20% by volume control serum and 20% by volume Zuoguiwan drug-containing serum were given to intervene for 48 h. After treatment, the MC3T3-E1 cells were first washed twice with pre - chilled PBS solution (4℃, 2 mL in volume), and then RIPA lysis solution was added to achieve cell lysis. After lysis was completed, cell supernatants were separated and collected by centrifugation at 14,000 rpm for 10 min at 4 degrees Celsius in order to extract total proteins, and the total protein concentration of each group was determined by BCA method. The protein samples were mixed well with 5× protein uploading buffer, and then heated in a metal bath at 99 ℃ for 10 min to denature the proteins completely. For electrophoresis, the amount of protein calculated after the BCA method was applied, and the proteins were transferred to a polyvinylidene difluoride membrane using electrotransfer technology, and the transferred membrane was closed with a sealing solution for 1 h. After that, the appropriately diluted primary antibody anti-GAPDH antibody（abcam，1：15000，ab181602），anti-NFKB antibody（proteintech，1：1000，15506-1-AP），anti-EGFR antibody（proteintech，1：3000，18986-1-AP），anti-Albumin antibody（proteintech，1：30000，16475-1-AP），anti-STAT3 antibody（proteintech，1：3000，10253-2-AP）anti-MMP9 antibody（proteintech，1：1000，27306-1-AP），anti-HIF1a antibody（proteintech，1：5000，80933-1-RR），anti-TLR4 antibody（proteintech，1：2000，19811-1-AP）were incubated at 4 °C cold storage on a shaker overnight for incubation. On the following day, the antibody was washed three times with TBST buffer for 5 min each time for a continuous period of time, HRP-labelled secondary antibody was added at a dilution of 1:1000, and the antibody was washed three times for 5 min each time after incubation for 1 h at room temperature, and finally, the colour reaction was carried out by using ultrasensitive ECL chemiluminescent reagent, and the images of the bands were captured by a gel-imaging system. The bands were analysed using ImageLab 5.2 software, and the relative ratio of the grey value of the target protein to that of the internal reference protein GAPDH was calculated as the corrected protein expression. 2.8 Statistical analysis For statistical evaluation of the experimental results, we used GraphPad Prism version 9.0. t-tests were used to compare differences between two groups, while one-way analysis of variance (ANOVA) was used for analyses involving multiple groups. Data are reported as mean ± standard deviation (SD). Statistical significance was established at a threshold of p0.05, * for p<0.05, ** for p<0.01, *** for p<0.001, and *** for p<0.0001. 3 Results 3.1 ZGW metabolite Identification According to the chromatographic conditions, positive and negative ionograms were collected from the control mode (Fig. 2A, B), positive and negative ionograms of the original solution of Zuo Gui Wan Tang (Fig. 2C, D), positive and negative ionograms of the serum of control rats (Fig. 2E, F) and positive and negative ionograms of the serum of ZGW-treated rats (Fig. 2G, H). 3.2 UPLC-MS/MS analysis of the active metabolites of ZGW-containing sera The precise mass-to-charge ratio (m/z) of the metabolites was obtained by UPLC-MS and the secondary fragmentation ions of this mass were obtained by secondary mass spectrometry. Multidimensional analyses were carried out to confirm the identified metabolites through factors such as precision of the mass of the parent ions, secondary fragmentation matches, isotopic distributions and peak intensities. The TCMSP database (https://tcmsp-e.com/tcmsp.php), HERB database (http://herb.ac.cn), and etcm database (http://www.tcmip.cn/ETCM/index.) were used to establish a database of Zuo Gui Wan compositions, and by comparing with UPLC- MS results to detect 209 chemical metabolites of the Chinese herbal preparation ZGW. It was set that differences in drug-containing serum exceeding five times that of control serum were considered significant, and 20 significant duplicate metabolites were observed in drug-containing serum. Table 3 provides the Major drug-containing serum metabolites of Zuo Gui Wan (ZWG) identified by LC-MS/MS. Detailed fragmentation patterns are provided in Supplementary 1 Table S1.A detailed list of the metabolites identified in the ZGW decoction is presented in Supplementary 2 and Supplementary 3. Table 3. Major drug-containing serum metabolites of Zuo Gui Wan (ZWG) identified by LC-MS/MS. No. Metabolite m/z Retention time (min) Adduct Mass Error (ppm) 1 7-epi-loganin 229.107 13.42 M+H 0.15 2 8-epiloganic acid 375.1306 10.21 M-H 2.49 3 8-epiloganin deglycosylation 390.1525 13.42 M+NH4 1.48 4 Catalpol 407.1203 3.13 M+FA-H 2.06 5 Dehydromevalonic lactone 205.0818 0.91 M+Na 8.33 6 Dl-tyrosine 213.1231 7.36 M+NH4 3.85 7 Farnesylacetone 456.2954 27.98 M+NH4 1.5 8 Geniposide 401.1098 12.68 M-H 2.12 9 Mellitoxin 369.0834 9.32 M+FA-H 1.75 10 Methyl (trihydroxy-cyclopentapyran carboxylate) 288.1074 1.53 M+NH4 3.34 11 Methyl 3-hydroxy-1-methyl-hexahydropyran carboxylate 429.1368 9.97 M+Na 0.07 12 Phenylalanine 185.1286 11.44 M+NH4 2.35 13 Rehmaionoside a 380.2617 24.63 M+NH4 8.36 14 Sweroside 403.1256 12.46 M+FA-H 2.57 15 Tryptophan 219.1128 12.08 M+H 0.06 16 2-amino-2-deoxy-alpha-D-glucopyranose 184.0427 5.67 M+Na 1.15 17 2-phenylethanol 163.0866 5.35 M+Na 8.98 18 2-phenylpropionic acid 344.1339 10.09 M+NH4 1.96 19 3,4-dihydroxybenzoic acid 200.0554 5.11 M+NH4 2.47 20 (2s,3r,4r,5s,6r)-2-[[(1s,4as,5r,7ar)-4a,5-dihydroxy-7-(hydroxymethyl)-5] 357.0839 11.84 M-H 3.19 Notes: Only the major metabolites are listed here. Full fragmentation patterns are provided in Supplementary Table S1. Retention time (RT) and mass-to-charge ratio (m/z) were obtained using LC-MS/MS. Adducts and mass error (ppm) indicate measurement accuracy. 3.3 Identification of ZGW metabolites and Target Prediction Through mass spectral analysis of TCM metabolite samples, 209 chemical metabolites were detected. From these, 20 migrated metabolites were pinpointed in the serum samples, based on their in vitro chemical profiles and primary and secondary metabolites. Isomeric and Canonical SMILES for these metabolites were retrieved from PubChem. Target prediction was carried out using the Swiss Target Prediction and Super-PRED databases. Swiss Target Prediction revealed 532 targets with a probability greater than zero, and Super-PRED indicated 1,187 targets with a probability of 60% or higher. After establishing the metabolite-target correlations, a total of 1,648 unique targets were identified, which was reduced to 464 distinct targets upon removing redundant metabolite-target associations. 3.4 ZGW Improves Bone Loss and Tissue Pathological Damage, and Modulates Serum Bone Metabolic Factors in OVX Rats To simulate PMOP, we used the ovariectomized (OVX) method to establish the model. By analyzing femoral micro-CT images of different groups (Fig 3A), we observed that the NC and SHAM groups displayed normal trabecular bone structures, while the OVX group showed significant trabecular atrophy, fragmentation, and irregular distribution, accompanied by an expansion of the marrow cavity. After treatment with different doses of Zuo Gui Wan (ZGW-L, ZGW-M, ZGW-H) and alendronate sodium (ALN), the microstructure of the femur was significantly improved, and the integrity of the trabeculae was restored. Notably, the mid- and high-dose groups showed significant improvements in trabecular spacing (Tb.Sp), trabecular number (Tb.N), and bone volume fraction (BV/TV) (Fig 3B-G). To further assess the effect of ZGW on trabecular structure, we performed HE staining of the femur. As shown in Fig 4A, the NC and SHAM groups exhibited an orderly trabecular structure with a well-organized marrow cavity, whereas the OVX group showed an enlarged marrow cavity, disordered trabecular structure, and new adipocyte formation. HE staining results also indicated that treatment with mid- and high-dose ZGW or ALN improved the pathological changes in bone tissue induced by ovariectomy. ELISA quantitative analysis of serum bone metabolic markers showed that the levels of BLAP, E2, and BGP in the OVX group were significantly lower than those in the NC and SHAM groups, while CTX-I was significantly higher in the OVX group, confirming successful modeling. Compared to the OVX group, the ZGW-H and ALN groups showed significantly increased levels of BLAP, E2, and BGP, indicating the osteogenic effect of ZGW. Additionally, compared to the OVX group, the levels of CTX-I in the ZGW-M, ZGW-H, and ALN groups were significantly reduced, indicating that ZGW significantly inhibited bone resorption (Fig 4B-E). 3.5 network analysis Analysis 3.5.1 Prediction of ZGW targets for PMOP treatment Targets relevant to PMOP were sourced from multiple databases. Specific counts included 4 from the Therapeutic Target Database (TTD), 78 from DrugBank, 1,118 from GeneCards, 45 from Online Mendelian Inheritance in Man (OMIM), and 171 from DisGeNET. Additionally, the top 1,000 most relevant genes were selected from the GSE230665 dataset. After consolidating and deduplicating these sources, a total of 2,316 targets associated with PMOP were compiled (Fig. 3A).Taking the intersection of the 464 ZGW predicted targets with the 2316 PMOP-associated targets produced 144 cross-targets for ZGW treatment of PMOP (Fig. 3B). 3.5.2 Construction of the "Herb-metabolite-Target-Disease" Network This dataset, including 20 active ZGW metabolites known to impact PMOP targets and the drug names, was imported into Cytoscape 3.8.0 to establish the “botanical drug-metabolite-Target-Disease” network (Fig.5C). Within this network, the centrality of nodes is indicated by node betweenness, where higher values signify greater importance. Rehmaionoside A exhibited the highest node betweenness, recorded at 1488.0874, followed by Farnesylacetone, Tryptophan, and 3,4-Dihydroxybenzoic Acid with betweenness scores of 1209.7041, 804.7451, and 486.71744, respectively. These prominent metabolites may play key roles in ZGW’s efficacy against PMOP. Table 4 lists the top 10 active metabolites as ranked by node betweenness. Table 4. The top 10 active metabolites ranked by node betweenness Molecule name Betweenness Rehmaionoside a 1488.0874 Farnesylacetone 1209.7041 Tryptophan 804.7451 3,4-dihydroxybenzoic acid 486.71744 Geniposide 413.7369 Dl-tyrosine 338.42392 Sweroside 303.73923 Phenylalanine 264.03958 Mellitoxin 249.03209 Catalpol 229.59859 3.5.3 PPI Analysis of Intersecting Targets A PPI network was built by analyzing the 144 overlapping targets between ZGW and PMOP, which was made possible by the STRING 11.0 database. With 144 nodes and 1494 edges, this network shows how the intersecting targets interact with one another. Node stands for a target and edge for the interactions between them. Cytoscape 3.8.0 was used to visualize the PPI network （Fig.6A）, and the CytoNCA plugin was used to determine the core targets by assessing the centrality of the target set. The ten most central genes, listed by their degree of connectivity (Table 5). A Sankey diagram was created to depict the links between ZGW, these ten core genes, and the five metabolites most closely associated with these core genes （Fig.6B）. Table 5. Top ten core targets of the PPI network for the ZWG and PMOP intersection targets Rank Target Degree Betweenness Closeness 1 GAPDH 90 2725.013 0.71649486 2 ALB 80 1680.9844 0.6780488 3 ESR1 68 1000.3308 0.640553 4 EGFR 67 769.21936 0.640553 5 PTGS2 66 1053.1273 0.6347032 6 MMP9 65 595.7026 0.61777776 7 STAT3 65 456.10052 0.6233184 8 NFKB1 62 368.08936 0.62053573 9 HIF1A 61 453.73227 0.6043478 10 TLR4 59 482.62494 0.6096491 3.5.4 GO enrichment and KEGG pathway analysis We used GO and KEGG enrichment analysis to learn more about the biological processes that make ZGW work to treat PMOP. Important biological processes (BP)（Fig.7A）, cellular metabolites (CC)（Fig.7B）, and molecular functions (MF)（Fig.7C） were identified. Key biological processes included responses to peptides, nutrients, extracellular stimuli, and hypoxia. Changes in cellular metabolites were primarily linked to membrane rafts, membrane microdomains, vesicle lumens, and secretory granule lumens. Functional molecular interactions were mostly linked to nuclear receptor, ligand-activated transcription factor, and serine-type peptidase activities.Fig.5D details the top 25 pathways, out of 158 important signaling pathways (P<0.05) identified by the KEGG pathway enrichment analysis. Important routes that have been found for ZGW to exert its therapeutic benefits on PMOP include the HIF-1 pathway, the estrogen pathway, the PI3K-Akt pathway, and cellular senescence. 3.5.5 Molecular Docking Molecular docking studies were performed to evaluate the binding efficiency of four core metabolites—Dehydromevalonic lactone, 3,4-dihydroxybenzoic acid, 2-phenylethanol, 2-amino-2-deoxy-alpha-D-glucopyranose, and DL-tyrosine—with ten corresponding hub genes (ALB, EGFR, ESR1, PTGS2, MMP9, STAT3, GAPDH, NFKB1, TLR4, and HIF1A). Binding affinity was analyzed, with higher absolute values indicating stronger binding capabilities (negative values indicate favorable binding). Table 6 summarizes the binding energies between these core metabolites and the hub genes. Specifically, 3,4-dihydroxybenzoic acid demonstrated significant docking effects with MMP9, PTGS2, and EGFR, exhibiting binding energies of -8.7, -7.2, and -7.1 kcal/mol, respectively. DL-tyrosine showed notable binding energies of -7.3 and -6.9 kcal/mol with PTGS2 and HIF1A, respectively. The molecular interactions were visualized in 3D using PyMol 2.4.0. Fig8.A-F depict the interactions between 3,4-dihydroxybenzoic acid and NFKB1, MMP9, HIF1A, and between DL-tyrosine and STAT3, PTGS2, and NFKB1, respectively. Table 6. Table of molecular docking binding energies between core metabolites and corresponding core targets metabolite Target respective binding energies Dehydromevalonic lactone ALB -5.1 Dehydromevalonic lactone EGFR -5.1 Dehydromevalonic lactone ESR1 -5.3 3,4-dihydroxybenzoic acid PTGS2 -7.2 3,4-dihydroxybenzoic acid EGFR -7.1 3,4-dihydroxybenzoic acid MMP9 -8.7 3,4-dihydroxybenzoic acid STAT3 -6.5 3,4-dihydroxybenzoic acid GAPDH -6.3 3,4-dihydroxybenzoic acid NFKB1 -7 3,4-dihydroxybenzoic acid TLR4 -6.8 3,4-dihydroxybenzoic acid HIF1A -5.5 2-phenylethanol NFKB1 -6 2-phenylethanol HIF1A -4.8 2-phenylethanol TLR4 -5.4 2-amino-2-deoxy-alpha-d-glucopyranose NFKB1 -6.4 2-amino-2-deoxy-alpha-d-glucopyranose HIF1A -5.8 2-amino-2-deoxy-alpha-d-glucopyranose TLR4 -5.7 Dl-tyrosine HIF1A -6.9 Dl-tyrosine NFKB1 -6.4 Dl-tyrosine PTGS2 -7.3 Dl-tyrosine TLR4 -5.8 Dl-tyrosine STAT3 -6.4 3.6 In vitro experimental validation 3.6.1 Safety of Drug-Containing Serum and Its Effect on the Proliferation of MC3T3-E1 Cells Cell viability was assessed by converting OD values using the formula: [(experimental group - blank group) / (control group - blank group)]. This calculation facilitated comparisons of the impact of drug-containing serum on MC3T3-E1 cell proliferation. The group exposed to serum containing 20% of the medication had the highest cell viability 48 hours after treatment (Fig. 11), this group's viability was much greater than the control serum group's. Under the same time and serum concentration, the cell viability of the drug containing serum group was generally higher than that of the control serum group. After 48 hours of intervention with drug containing serum at each concentration, the cell viability was higher than that of the control serum group at the corresponding concentration and time (P<0.05). After 48 hours of equal intervention, the 20% drug containing serum group was higher than other concentration groups(P<0.05)，Consequently, a 48-hour exposure at 20% concentration of drug-containing serum was selected for further experiments. 3.6.2 Effect of ZGW-containing serum on alkaline phosphatase activity in MC3T3-E1 cells The absorbance values were converted to alkaline phosphatase activity values (ALP) according to the instructions, and the 20% Zuo Gui Wan-containing serum intervention group could up-regulate the cellular alkaline phosphatase activity to a certain extent compared with the control serum intervention group (P < 0.01).(Fig.7B) 3.6.3 Effect of ZGW-containing serum on osteogenic differentiation of MC3T3-E1 cells Alizarin red S staining was performed after 20 d of osteogenic induction, and under 100x microscope, the number of red calcium nodules in the Zuo Gui Wan containing serum intervention group was significantly more than that in the control serum intervention group (Fig.9C-D), and the staining area was statistically analysed by imagej, and the staining area of the 20% Zuo Gui Wan containing serum group with a volume fraction of 20% Zuo Gui Wan was better than that of the 20% control serum group (P < 0.01) (Fig.9E)) 3.6.4 Effect of ZGW drug-containing serum on core target mRNAs We used RT-qPCR to measure mRNA expression of target proteins in MC3T3-E1 cells treated with 20% control rat serum (NC group) and 20% drug-containing serum (ZGW group).Compared with the NC group, the mRNA levels of ALB, EGFR, and MMP9 were statistically significantly higher in the ZGW group, and the mRNA levels of STAT3, NFKB1, TLR4, and HIF-1α were statistically significantly lower (Fig. 10A).Compared with the NC group, the mRNA of PI3K-AKT-mTOR-related pathway proteins: PI3KR1, PI3KCA, PI3KCB, NOS3 were up-regulated, and MTOR was decreased in the ZGW group, with a statistically significant difference (Fig.10B), and the mRNA of bone-marking proteins: OPG, OSTERIX, RUNX2 were up-regulated in the ZGW group compared with the NC group.The difference was statistically significant (Fig.10C), ZGW may have a therapeutic effect on PMOP by affecting core genes that are essential for osteoblast differentiation and cell proliferation. 3.6.5 Effect of ZGW-containing serum on core target proteins We used WB method to measure the expression of target proteins in MC3T3-E1 cells treated with 20% control rat serum (NC group) and 20% drug-containing serum (ZGW group).Compared with the NC group, the protein levels of ALB and MMP9 were statistically significantly higher in the ZGW group, and the protein levels of EFGR, STAT3, NFKB1, TLR4 and HIF-1α were statistically significantly lower (Fig. 11A).Based on the quantitative analysis of ZGW-containing serum for relevant proteins(Fig.11B), Compared with the NC group, the ZGW metabolite bone marker proteins: OPG, OSTERIX, and RUNX2 were increased with a statistically significant difference (Fig. 11B), and ZGW may have a therapeutic effect on PMOP by affecting core genes that are essential for osteoblast differentiation and cell proliferation.To further investigate the effects of ZGW on the PI3K and HIF pathways, we used WB methods to measure the expression of relevant proteins in BMSCS cells treated with 20% blank rat serum (NC group) and 20% drug-containing serum (ZGW group) (Fig. 11D, E), and the results showed that ZGW activated the PI3K and HIF pathways in BMSCS cells, and promoted osteogenic protein expression. 3.6.6 ZGW Exerts Its Effect via the PI3K and HIF Pathways Based on previous experimental results, we used 10 µmol LY294002 and 100 µmol DMOG to intervene in the cells to verify that Zuo Gui Wan (ZGW) promotes osteogenesis by activating the PI3K signaling pathway and HIF signaling pathway. As shown in Fig 12, cells were treated separately with LY294002 and DMOG to verify the effectiveness of the inhibitors. In Fig 12, ZGW was found to elevate the protein levels of Runx2 and OPG, and LY294002 inhibited this effect. Additionally, DMOG treatment partially reversed the inhibitory effect of LY294002, although this reversal was not significant. Further studies are needed to explore the underlying mechanisms. 4 Discussion The symptoms of osteoporosis, a metabolic bone illness that affects the entire body, include a decrease in bone mass, degradation of microarchitecture, and an elevated risk of fractures[ 16] . Globally, it affects approximately 200 million people as per the International Osteoporosis Foundation[ 17] . In China, the prevalence among women over 50 years old is 32.1%, increasing to 51.6% in women over 65. Alternatively, in the US and EU, about 30% of women experience PMOP, which greatly affects the survival rate of older women. Hip fractures are among the most deadly osteoporotic fractures, although they can occur in any bone in the body[ 18] . Current pharmacological interventions primarily include bisphosphonates such as alendronate and risedronate, which suppress osteoclast activity and mitigate bone loss. In contrast to calcitonin, which inhibits osteoclasts to decrease bone resorption, selective estrogen receptor modulators (SERMs) such as raloxifene increase bone density by mimicking estrogen's effects. Parathyroid hormone analogs such as teriparatide stimulate bone formation, increasing bone density. Additionally, anti-RANKL antibodies like denosumab obstruct RANKL[ 19] to reduce osteoclast formation. Despite their efficacy, these treatments often entail side effects, such as increased risk of gastric ulcers and esophagitis from alendronate or high costs, making them inaccessible in certain regions. TCM has long been utilized in treating osteoporosis, with various botanical drugs known to relieve joint pain and fortify bones and muscles. For example, silybin from Silybi Fructus boosts osteoblast proliferation and differentiation, enhances alkaline phosphatase mineralization and upregulates the transcription of osteocalcin, Runx-2, SOD-2, and SIRT1 under iron overload[ 20] . Morinda officinalis oligosaccharides increase OPG mRNA and protein levels while reducing RANKL mRNA and protein expression in bone tissues, influencing the OPG/RANKL/RANK pathway to counteract osteoporotic effects[ 21] . ZGW, based on "Jingyue Quanshu·New Prescriptions Eight Arrays," is widely used in clinical settings to address osteoporosis[ 22] . It consists of eight metabolites, including Cuscutae Semen, Dioscoreae Rhizoma, Lycii Fructus, Cervi Cornus Colla, Rehmanniae Radix Praeparata, Corni Fructus, Testudinis Carapacis Et Plastri Colla, and Cyathulae Radix, traditionally employed to alleviate symptoms such as lumbar acid, leg softness, and internal deficiency due to yin deficiency. Notably, Cuscutae Semen protects osteoblasts and restricts osteoclastogenesis, mitigating glucocorticoid-induced osteoporosis[ 23] , while new glycosides from Corni Fructus promote osteoblast proliferation and differentiation, regulating genes and proteins involved in osteogenic pathways[ 24] . However, the precise metabolites and mechanisms underlying ZGW’s effects on PMOP remain elusive. This study is the first of its kind to employ UPLC-MS/MS, network analysis, and molecular docking to determine the targets and core metabolites of ZGW and to explain how it may operate. In vivo cellular experiments using MMT, ALP, alizarin red S staining, PCR, and WB to verify the osteogenic effect of ZWG. In this study, we applied UPLC-MS/MS technology to identify 209 metabolites within ZGW decoction and detected 20 additional metabolites in serum samples. Using the Swiss Target Prediction and Super-PRED databases, we predicted 464 targets. Moreover, we retrieved 2,316 PMOP-related targets from the databases DrugBank, GeneCards, TTD, DisGeNET, OMIM, and GEO. The intersection of ZGW and PMOP targets revealed 144 common targets. In our constructed botanical drug-metabolite-target-disease network, certain metabolites such as Rehmaionoside A, Farnesylacetone, Tryptophan, 3,4-dihydroxybenzoic acid, and Geniposide emerged as having numerous targets related to PMOP, suggesting their potential as core metabolites of ZGW. Rehmaionoside A, a terpenoid glycoside found predominantly in Rehmanniae Radix Praeparata, exhibits anti-osteoporotic properties by regulating kidney and liver functions and enhancing blood circulation[ 25] . Farnesylacetone, a terpene ketone, functions as a hormone and metabolite, playing a role in the synthesis of macromolecules in crustacean gonads[ 26] . The amino acid tryptophan influences osteoporosis via regulating the metabolism of SCFAs and TMAO. It is a precursor to the neurotransmitters serotonin, melatonin, and kynurenine[ 27] . Its metabolism via the kynurenine pathway impacts osteoblastogenesis, with certain oxidation products inhibiting the proliferation and differentiation of Bone Marrow Stromal Cells (BMSCs) and osteoblasts[ 28] . 3,4-dihydroxybenzoic acid, positioned hydroxyl groups at the 3rd and 4th locations, serves as an exogenous metabolite and antitumor agent in humans. The chemical has a dual effect during osteogenesis—it increases intracellular mineralization—and adipogenesis—it decreases lipid accumulation in BMSCs and MC3T3-E1 cells[ 29] . It promotes osteogenesis and inhibits adipogenesis, contributing to osteoporosis treatment. In addition to suppressing the expression of osteoclast-specific markers like MMP, c-Src, and the transcription factors AP-1, 3,4-dihydroxybenzoic acid induces osteoclast apoptosis by means of mitochondrial membrane potential alterations, and caspase activation[ 30] . Conversely, geniposide, a terpenoid glycoside present in Rehmanniae Radix Praeparata and other botanical drugs, mitigates endoplasmic reticulum stress and reduces dexamethasone-induced osteoblast apoptosis. It enhances mitochondrial resilience against dexamethasone-induced apoptosis in MC3T3-E1 cells[ 31] by upregulating the NRF2 pathway and downregulating the NF-κB pathway, while activating the GLP-1R / ABCA1 and ERK signaling pathways[ 32] . This facilitates the alleviation of glucocorticoid-induced osteogenic differentiation inhibition, inhibits c-Fos protein hydrolysis, and prevents IκB degradation, thereby reducing RANKL-induced osteoclast differentiation and mitigating osteoporosis progression[ 33] . In this study, we established a PPI network to identify common targets between ZGW and PMOP and evaluated the key gene functions via GO and KEGG pathway analyses. Within the PPI network, the genes ALB, EGFR, NFKB1, TLR4, STAT3, HIF1A, MMP9, and ESR1 exhibited elevated centrality, highlighting their potential as pivotal targets for ZGW’s efficacy in PMOP treatment. ALB (serum albumin) is a key protein in maintaining plasma osmolality and nutrient transport with antioxidant and calcium ion binding functions. Low serum ALB levels are significantly associated with decreased bone mineral density and increased fracture risk[ 34] 。ALB promotes osteoblast mineralization by binding calcium ions and IGF-1 and scavenges ROS to protect cells from oxidative damage[ 35] 。ALB promotes osteoblast mineralization by binding calcium ions and IGF-1 and scavenges ROS to protect cells from oxidative damage[ 36] .MMP9 (matrix metalloproteinase 9) is a metalloproteinase that degrades the extracellular matrix (ECM) and is involved in bone remodelling and angiogenesis.Enhanced activity of MMP9 is accompanied by collagen degradation in the ECM, releasing osteogenic differentiation-associated growth factors (e.g. TGF-β) that bind to the ECM, thus indirectly activating the BMP/Smad pathway to drive osteoblasts[ 37] ，However, long-term high expression inhibits osteogenic differentiation[ 38] .EGFR (epidermal growth factor receptor) EGFR is a tyrosine kinase receptor that regulates cell proliferation and survival and activates the MAPK and PI3K pathways.EGFR signalling enhances negative regulation of mTOR signalling to control the promotion of osteoblast differentiation.[ 39] 。At the same time, EGFR inhibitors can suppress bone resorption by inhibiting the process of polarisation of M1 macrophages towards osteoclast differentiation[ 40] ，reflecting the bidirectional benefits of EGFR signalling on osteogenesis.STAT3 (Signal Transducer and Activator of Transcription 3) is a transcription factor that plays a central role in a variety of pathological processes by regulating cell proliferation, apoptosis, and inflammatory responses.In osteoporosis, aberrant activation of STAT3 exacerbates the inflammatory microenvironment and stimulates the JAK/NF-κB pathwayinteraction, promoting osteoclast differentiation and inhibiting osteoblast activity. [ 41] 。NFKB1 (Nuclear Factor Kappa B Subunit 1) is a core subunit of the NF-κB signalling pathway, and aberrant activation of NFKB1 promotes osteoclast differentiation and inhibits osteoblast activity through the up-regulation of pro-inflammatory factors, such as TNF-α and IL-6, leading to bone resorption-formation imbalance[ 42] ，Inhibition of NFKB1 was shown to reduce RANKL expression by blocking the TLR4/MyD88/NF-κB pathway, thereby inhibiting osteoclastogenesis and alleviating bone loss[ 43] 。TLR4 (Toll-Like Receptor 4) is a pattern recognition receptor that mediates natural immune responses and chronic inflammation regulation through activation of the NF-κB and MAPK pathways.In osteoporosis, overexpression of TLR4 promotes the polarisation of M1-type macrophages, releases a large number of pro-inflammatory factors (e.g., IL-1β, TNF-α), inhibits osteoblast differentiation and accelerates boneResorption [ 44] 。HIF-1alpha (Hypoxia-Inducible Factor 1 Alpha) is a core transcription factor of the hypoxic response and is involved in the maintenance of bone homeostasis by regulating angiogenesis and energy metabolism.HIF-1alpha increases glycolytic responses and promotes osteoclastogenesis[ 45] ，Reducing HIF-1α protein expression reduces oxidative stress in osteoblasts[ 46] 。 Notably, based on KEGG pathway analysis, the PI3K signalling pathway becomes crucial.The PI3K (phosphatidylinositol 3-kinase) signalling pathway plays an important role in bone homeostasis by regulating cell proliferation, survival, metabolism and differentiation.The pathway consists of catalytic subunits (e.g. PI3KCA, PI3KCB) and regulatory subunits (e.g. PI3KR1), which activate downstream effector molecules, such as AKT/mTOR, by phosphorylating PIP2 to generate PIP3, and the up-regulation of PI3KR1/PI3KCA activates AKT signalling and promotes osteoblast proliferation and differentiation[ 47] ，Decreased MTOR activity may promote differentiation of MSCs towards osteogenesis by deregulating its regulation of osteogenic differentiation inhibitors such as PPARγ[ 48] ，NOS3 upregulation may affect bone repair by modulating angiogenesis and inflammatory microenvironment[ 49] 。 OSTERIX, RUNX2 and OPG are all osteogenic marker proteins, and OSTERIX is a transcription factor with a zinc finger structure that plays an important role in osteoblast differentiation and is an essential gene for osteoblast development[ 50] .RUNX2 is an important transcription factor involved in bone and cartilage development and is an essential gene in osteoblast and chondrocyte differentiation[ 51] .OPG is a lysophosphatidic acid-binding protein belonging to the tumour necrosis factor receptor family, which plays an important role in the regulation of bone remodelling and protects bone tissue by inhibiting osteoclast production and activity[ 52] 。 To evaluate the therapeutic effect of ZGW on PMOP, our first step was to measure the safety of ZGW-containing serum, screen the optimal serum-containing concentration, and assess its impact on the proliferation of MC3T3 cells using the MTT method. The results showed that the safety of ZGW-containing serum was good, 20% containing serum was the optimal concentration, and ZGW-containing serum had a statistically significant difference in promoting cell proliferation compared with control serum. Secondly, we detected its effect on the expression of cellular osteogenic marker alkaline phosphatase by ALP kit, and the results showed that the cellular ALP level was significantly increased under the intervention of 20% drug-containing serum. Next, we assessed the effect of ZGW-containing serum on osteogenic mineralisation using alizarin red S staining, and the results showed significant cellular mineralisation under 20% serum-containing intervention. We used quantitative RT-PCR to compare gene expression profiles between control serum control and serum samples containing the ZGW drug. We focused on key genes identified in the PPI network and genes related to the KEGG enrichment pathway in our network pharmacological analysis. Our results showed that mRNA levels of ALB, EGFR, and MMP9 were significantly higher in the ZGW-treated group, while STAT3, NFKB1, TLR4, and HIF-1α were significantly lower. These changes were consistent with predictions derived from network analysis and molecular docking studies, supporting the role of ZGW in enhancing osteogenesis and its therapeutic application in PMOP management. Further studies of genes related to the PI3K signalling pathway showed that IK3R1, PI3KCA, PI3KCB, and NOS3 were up-regulated and MTOR was decreased in the ZGW group, suggesting that ZGW may promote cellular osteogenic differentiation through activation of the PI3K signalling pathway. We further examined three osteogenic markers to support these findings. The expression levels of OSTERIX, RUNX2, and OPG were significantly elevated in the ZGW group, confirming the ability of ZGW to positively stimulate osteogenesis. This in-depth study supports the promise of ZGW as a treatment for PMOP by elucidating the molecular processes by which ZGW acts. We continued to validate the above gene targets using WB experiments and concluded that under the intervention of 20% drug-containing serum, the protein levels of ALB and MMP9 were statistically significantly elevated in the ZGW group, the protein levels of EFGR, STAT3, NFKB1, TLR4 and HIF-1α were statistically significantly reduced, suggesting that ZGW may reduce the release of inflammatory factors, improve the bone marrow hypoxic microenvironment, and promote osteoblast differentiation through inhibition of the STAT3/NF-κB/TLR4/HIF-1α axis.STAT3 synergistically drives the release of pro-inflammatory factors (TNF-α, IL-6) in conjunction with NF-κB signalling, and the activation of TLR4 further amplifies inflammatory cascade responses, and the inhibition of these pathways by ZGW that significantly reducing the level of inflammation in the bone microenvironment. Chronic hypoxia induces bone marrow mesenchymal stem cells (BMSCs) to differentiate into adipocytes through HIF-1α and activates glycolysis to inhibit osteogenesis.ZGW reduced HIF-1α expression, reversed hypoxia-driven metabolic reprogramming, and promoted MC3T3-directed differentiation towards the osteogenic lineage.ALB, as a carrier protein, binds and stabilises bone morphogenetic proteins (BMPs) and insulin-like growth factor (IGF-1), and enhances the activity of the PI3K-AKT-mTOR signalling pathway.ZGW may enhance the expression of ALB by up-regulating thePI3K-AKT-mTOR signalling pathway, providing metabolic support to osteoblasts and enhancing bone matrix mineralisation.MMP9 is involved in bone remodelling and angiogenesis, and ZGW may promote bone remodelling and angiogenesis by transiently elevating MMP9 to degrade collagen fibres in the bone matrix and releasing growth factors such as sequestered TGF-β and VEGF.EGFR has a dual benefit on osteogenesis, with early activation of the ERK/PI3K pathway, promoting down-dialled proliferation and RUNX2 expression, but later on, EGFR protein however, the continuous activation of EGFR protein in the later stage will lead to the inhibition of mineralisation. After ZGW intervention, the mRNA level of EGFR increased and the protein level decreased, which may be related to the precise regulation of "transcriptional activation-post-translational inhibition" of EGFR signalling, but the specific mechanism needs to be studied in depth in the future. This demonstrates the multi-target synergy of ZGW in PMOP treatment to promote osteogenic differentiation. 5 Conclusion ZGW addresses PMOP through a multifaceted approach involving various metabolites, targets, and metabolic pathways. Our research corroborated ZGW’s anti-PMOP effects and delineated its primary metabolites, crucial targets, and underlying mechanisms via methods such as UPLC-MS/MS, network analysis, molecular docking, and experimental protocols. ZGW influences genes including ALB, EGFR, NFKB1, TLR4, STAT3, HIF1A, MMP9, and ESR1, facilitating osteogenesis and curtailing the advancement of PMOP through the modulation of pathways such as PI3K-AKT. This investigation enhances our understanding of PMOP treatment and generates novel avenues for the development of anti-osteoporotic agents. Nevertheless, further studies are required to elucidate the principal active metabolites of ZGW and its more intricate mechanisms in combating PMOP. Declarations Supplementary Information The online version contains supplementary material Acknowledgements Not applicable. Clinical trial number not applicable Author’s contributions Experimental design and project conception: Yongli Dong, Peng Feng;Experimental implementation: Jinguang Gu, Chenhua Li, Bin Zhang;Experimental data statistics: Weikai Qin, Baoyu Qi; Paper writing: Jinguang Gu. All authors agreed to the final manuscript. Funding This work was supported by the China Academy of Chinese Medical Sciences (Special Project for Training Outstanding Young Scientific and Technological Talents) and the National Natural Science Foundation of China, under grant number ZZ17-YQ-012 and 82305278. Data availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The rat-based research described in this study received ethical approval from the Ethics Center of the Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences. (Approval No. 2025B036), adhering to the guidelines outlined in the EEC Directive of 1986 (86/609/EEC). Consent for publication Not applicable. Competing interests All authors state that they have no conflicts of interest regarding the publication of this paper. Authors’contributions 1 Department of orthopedic surgery, Wangjing Hospital of China Academy of Chinese Medical Sciences,No. 6, Wangjing Zhonghuan South Road, Chaoyang District, Beijing100102, People’s Republic of China. References Arceo-Mendoza R M , Camacho P M .Postmenopausal Osteoporosis: Latest Guidelines[J].Endocrinology and Metabolism Clinics of North America, 2021(2):50.DOI:10.1016/j.ecl.2021.03.009. Dennis,M,Black,et al.Postmenopausal Osteoporosis[J].New England Journal of Medicine, 2016.DOI:10.1056/nejmcp1513724. Salari N,Darvishi N,Bartina Y,et al.Global prevalence of osteoporosis among the world older adults:a comprehensivesystematic review and meta-analysis[J].J Orthopaed Surg Res,2021,16(01):669. Compston J , Cooper A , Cooper C ,et al.UK clinical guideline for the prevention and treatment of osteoporosis[J].Archives of Osteoporosis, 2017, 12(1).DOI:10.1007/s11657-017-0324-5. Khosla S , Hofbauer L C .Osteoporosis treatment: Recent developments and ongoing challenges[J].The Lancet Diabetes & Endocrinology, 2017, 5(11).DOI:10.1016/S2213-8587(17)30188-2. Xiaohu J, Su G, Yuying Z, Simin C, Wenyan W, Jingjing Y, Meiqiu Y, Jing L, Jie S, Suhong C, Guiyuan L. Traditional Chinese Medicine formula Wubi Shanyao Pills protects against reproductive aging by activating SIRT1/3 to reduce apoptosis. J Ethnopharmacol. 2024 Jan 10;318(Pt B):116976. doi: 10.1016/j.jep.2023.116976. Epub 2023 Jul 29. PMID: 37524234. Meng J, Lv Z, Sun C, Qiao X, Chen C. An extract of Lycium barbarum mimics exercise to improve muscle endurance through increasing type IIa oxidative muscle fibers by activating ERRγ. FASEB J. 2020 Sep;34(9):11460-11473. doi: 10.1096/fj.202000136R. Epub 2020 Jul 30. PMID: 33411401. Gao X, Liu Y, An Z, Ni J. Active metabolites and Pharmacological Effects of Cornus officinalis: Literature Review. Front Pharmacol. 2021 Apr 12;12:633447. doi: 10.3389/fphar.2021.633447. PMID: 33912050; PMCID: PMC8072387. Liu F , Tan F , Tong W ,et al.Effect of Zuoguiwan on osteoporosis in ovariectomized rats through RANKL/OPG pathway mediated by β2AR.[J].Biomedicine & Pharmacotherapy, 2018, 103:1052-1060.DOI:10.1016/j.biopha.2018.04.102. Yousefzadeh, N., Kashfi, K., Jeddi, S., & Ghasemi, A. (2020). Ovariectomized rat model of osteoporosis: a practical guide. EXCLI journal, 19, 89–107. https://doi.org/10.17179/excli2019-1990 Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0:a major update to the DrugBank database for 2018. Nucleic Acids Res (2018) 46(D1):D1074–82. doi: 10.1093/nar/gkx1037 Mok SRS, Mohan S, Grewal N, Elfant AB, Judge TA. A genetic database can be utilized to identify potential biomarkers for biphenotypic hepatocellular carcinoma-cholangiocarcinoma. J Gastrointest Oncol (2016) 7(4):570–9. doi: 10.21037/jgo.2016.04.01 Li YH, Yu CY, Li XX, Zhang P, Tang J, Yang Q, et al. Therapeutic target databaseupdate 2018: enriched resource for facilitating bench-to-clinicresearch of targeted therapeutics. Nucleic Acids Res (2018) 46(D1):D1121–7. doi: 10.1093/nar/gkx1076 Piñero J, Queralt-Rosinach N, Bravo A, Deu-Pons J, Bauer-Mehren A, Baron M, et al. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (2015) 2015(0):bav028. doi: 10.1093/database/bav028 Amberger JS, Bocchini CA, Scott AF, Hamosh A. OMIM.org: leveraging knowledge across phenotype-gene relationships. Nucleic Acids Res. 2019 Jan 8;47(D1):D1038-D1043. doi: 10.1093/nar/gky1151. PMID: 30445645; PMCID: PMC6323937. Walsh LJ, Lewis SA, Wong CA, Cooper S, Oborne J, Cawte SA, Harrison T, Green DJ, Pringle M, Hubbard R, Tattersfield AE. The impact of oral corticosteroid use on bone mineral density and vertebral fracture. Am J Respir Crit Care Med. 2002 Sep 1;166(5):691-5. doi: 10.1164/rccm.2110047. PMID: 12204867. Cole ZA, Dennison EM, Cooper C. Osteoporosis epidemiology update. Curr Rheumatol Rep. 2008 Apr;10(2):92-6. doi: 10.1007/s11926-008-0017-6. PMID: 18460262. Dewan N , Macdermid J C , Grewal R ,et al.Risk factors predicting subsequent falls and osteoporotic fractures at 4years after distal radius fracture—a prospective cohort study[J].Archives of Osteoporosis, 2018, 13(1):32.DOI:10.1007/s11657-018-0445-5. Starup-Linde J , Langdahl B , Vestergaard P ,et al.Incident peptic ulcers and concomitant treatment of direct oral anticoagulants and oral bisphosphonates—a real-world cohort study[J].Osteoporosis International, 2022, 33(6):1323-1334.DOI:10.1007/s00198-022-06315-z. Jang S A , Hwang Y H , Kim T ,et al.Water Extract of Agastache rugosa Prevents Ovariectomy-Induced Bone Loss by Inhibiting Osteoclastogenesis[J].Foods, 2020, 9(9):1181.DOI:10.3390/foods9091181. Chen Y, Yang C, Deng Z, Xiang T, Ni Q, Xu J, Sun D, Luo F. Gut microbially produced tryptophan metabolite melatonin ameliorates osteoporosis via modulating SCFA and TMAO metabolism. J Pineal Res. 2024 Apr;76(3):e12954. doi: 10.1111/jpi.12954. PMID: 38618998. Liu F, Tan F, Tong W, Fan Q, Ye S, Lu S, Teng Z, Han M, Zhang M, Chai Y. Effect of Zuoguiwan on osteoporosis in ovariectomized rats through RANKL/OPG pathway mediated by β2AR. Biomed Pharmacother. 2018 Jul;103:1052-1060. doi: 10.1016/j.biopha.2018.04.102. Epub 2018 Apr 25. PMID: 29710663. Mo H , Zhang N , Li H ,et al.Beneficial effects of Cuscuta chinensis extract on glucocorticoid-induced osteoporosis through modulation of RANKL/OPG signals[J].Brazilian Journal of Medical and Biological Research, 2019, 52(12).DOI:10.1590/1414-431X20198754. HUANG Jia-chun,LIN Yan-ping,CHEN Tong-ying,et al. Effects of cornelianoside Ⅰ on the proliferation and osteogenic differentiation of osteoblasts[J]. Chinese Journal of Osteoporosis,2020,26(01):1-5. Liu C, Ma R, Wang L, Zhu R, Liu H, Guo Y, Zhao B, Zhao S, Tang J, Li Y, Niu J, Fu M, Zhang D, Gao S. Rehmanniae Radix in osteoporosis: A review of traditional Chinese medicinal uses, phytochemistry, pharmacokinetics and pharmacology. J Ethnopharmacol. 2017 Feb 23;198:351-362. doi: 10.1016/j.jep.2017.01.021. Epub 2017 Jan 19. PMID: 28111216. Berreur-Bonnenfant J, Lawrence F. Comparative effect of farnesylacetone on macromolecular synthesis in gonads of crustaceans. Gen Comp Endocrinol. 1984 Jun;54(3):462-8. doi: 10.1016/0016-6480(84)90163-1. PMID: 6203810. Chen Y, Yang C, Deng Z, Xiang T, Ni Q, Xu J, Sun D, Luo F. Gut microbially produced tryptophan metabolite melatonin ameliorates osteoporosis via modulating SCFA and TMAO metabolism. J Pineal Res. 2024 Apr;76(3):e12954. doi: 10.1111/jpi.12954. PMID: 38618998. Michalowska M, Znorko B, Kaminski T, Oksztulska-Kolanek E, Pawlak D. New insights into tryptophan and its metabolites in the regulation of bone metabolism. J Physiol Pharmacol. 2015 Dec;66(6):779-91. PMID: 26769827. Rivera-Piza A, An YJ, Kim DK, Lee SH, Kim JB, Choi JS, Lee SJ. Protocatechuic Acid Enhances Osteogenesis, but Inhibits Adipogenesis in C3H10T1/2 and 3T3-L1 Cells. J Med Food. 2017 Mar;20(3):309-319. doi: 10.1089/jmf.2016.3833. PMID: 28296593. Wu YX, Wu TY, Xu BB, Xu XY, Chen HG, Li XY, Wang G. Protocatechuic acid inhibits osteoclast differentiation and stimulates apoptosis in mature osteoclasts. Biomed Pharmacother. 2016 Aug;82:399-405. doi: 10.1016/j.biopha.2016.05.008. Epub 2016 May 31. PMID: 27470378. Xiao Y, Ren Q, Zheng Y, Zhang S, Ouyang J, Jiao L, Tang C, Li L, Shi W, Wang M, Zhang S, Zhang D, Zhong B, Peng F, Chen Z, Wu L. Geniposide ameliorated dexamethasone-induced endoplasmic reticulum stress and mitochondrial apoptosis in osteoblasts. J Ethnopharmacol. 2022 Jun 12;291:115154. doi: 10.1016/j.jep.2022.115154. Epub 2022 Feb 28. PMID: 35240241. Xiao Y, Zhang S, Ye Y, Chen J, Xu Y. Geniposide suppressed OX-LDL-induced osteoblast apoptosis by regulating the NRF2/NF-κB signaling pathway. J Orthop Surg Res. 2023 Aug 30;18(1):641. doi: 10.1186/s13018-023-04125-5. PMID: 37649066; PMCID: PMC10466864. Lee CH, Kwak SC, Kim JY, Oh HM, Rho MC, Yoon KH, Yoo WH, Lee MS, Oh J. Genipin inhibits RANKL-induced osteoclast differentiation through proteasome-mediated degradation of c-Fos protein and suppression of NF-κB activation. J Pharmacol Sci. 2014;124(3):344-53. doi: 10.1254/jphs.13174fp. PMID: 24646621. Li Q, Zhu M, Liu X, Tian C, Li D, Wang H, Liu H. Abnormally low serum albumin levels are associated with abnormal bone mineral density and osteoporotic fractures: a retrospective studies. BMC Musculoskelet Disord. 2024 Nov 7;25(1):888. doi: 10.1186/s12891-024-08021-9. PMID: 39511536; PMCID: PMC11542386. Sun Q, Zhang C, Hu G, Zhu K, Zheng S. Albiflorin improves osteoporotic bone regeneration by promoting osteogenesis-angiogenesis coupling of bone marrow mesenchymal stem cells. Biochem Biophys Res Commun. 2025 Mar 25;754:151551. doi: 10.1016/j.bbrc.2025.151551. Epub 2025 Feb 26. PMID: 40022815. Liu M, Zhang H, Li Y, Huang D, Zuo H, Yang J, Chen Z. Loss of MMP9 disturbs cranial suture fusion via suppressing cell proliferation, chondrogenesis and osteogenesis in mice. Matrix Biol. 2024 Dec;134:93-106. doi: 10.1016/j.matbio.2024.10.003. Epub 2024 Oct 5. PMID: 39374863. Fernandes CJDC, de Almeida GS, Wood PF, Gomes AM, Bezerra FJ, Vieira JCS, Padilha PM, Zambuzzi WF. Mechanosignaling-related angiocrine factors drive osteoblastic phenotype in response to zirconia. J Trace Elem Med Biol. 2024 Jan;81:127337. doi: 10.1016/j.jtemb.2023.127337. Epub 2023 Nov 4. PMID: 38000168. Da W, Jiang W, Tao L. ROS/MMP-9 mediated CS degradation in BMSC inhibits citric acid metabolism participating in the dual regulation of bone remodelling. Cell Death Discov. 2024 Feb 14;10(1):77. doi: 10.1038/s41420-024-01835-5. PMID: 38355572; PMCID: PMC10866869. Linder M, Hecking M, Glitzner E, Zwerina K, Holcmann M, Bakiri L, Ruocco MG, Tuckermann J, Schett G, Wagner EF, Sibilia M. EGFR controls bone development by negatively regulating mTOR-signaling during osteoblast differentiation. Cell Death Differ. 2018 Jun;25(6):1094-1106. doi: 10.1038/s41418-017-0054-7. Epub 2018 Feb 14. PMID: 29445126; PMCID: PMC5988706. Jia Q, Liu L, Yu Y, Wulamu W, Jia L, Liu B, Zheng H, Peng Z, Zhang X, Zhu R. Inhibition of EGFR Pathway Suppresses M1 Macrophage Polarization and Osteoclastogenesis, Mitigating Titanium Particle-Induced Bone Resorption. J Inflamm Res. 2024 Nov 26;17:9725-9742. doi: 10.2147/JIR.S484529. PMID: 39624396; PMCID: PMC11609397. Xiu C, Zhang L, Zhang C, Zhang Y, Luo X, Zhang Z, Zhao H, Ji K, Chen Z, He G, Chen J. Pharmacologically targeting fatty acid synthase-mediated de novo lipogenesis alleviates osteolytic bone loss by directly inhibiting osteoclastogenesis through suppression of STAT3 palmitoylation and ROS signaling. Metabolism. 2025 Mar 11;167:156186. doi: 10.1016/j.metabol.2025.156186. Epub ahead of print. PMID: 40081616. Yu T, Liu X, Jiang M, Li Y, Su H, Niu B. Cucumber seed polypeptides regulate RANKL-induced osteoclastogenesis through OPG/RANKL/RANK and NF-κB. In Vitro Cell Dev Biol Anim. 2024 Jan;60(1):54-66. doi: 10.1007/s11626-023-00834-7. Epub 2023 Dec 20. PMID: 38123756; PMCID: PMC10858069. Liu C, Zuo M, Zhao J, Niu T, Hu A, Wang H, Zeng X. DPHB inhibits osteoclastogenesis by suppressing NF-κB and MAPK signaling and alleviates inflammatory bone destruction. Int Immunopharmacol. 2025 Apr 16;152:114377. doi: 10.1016/j.intimp.2025.114377. Epub 2025 Mar 4. PMID: 40043357. Zhang Y, He X, Wang K, Xue Y, Hu S, Jin Y, Zhu G, Shi Q, Rui Y. Irisin alleviates obesity-induced bone loss by inhibiting interleukin 6 expression via TLR4/MyD88/NF-κB axis in adipocytes. J Adv Res. 2025 Mar;69:343-359. doi: 10.1016/j.jare.2024.04.013. Epub 2024 Apr 16. PMID: 38626873. Chu W, Peng W, Lu Y, Liu Y, Li Q, Wang H, Wang L, Zhang B, Liu Z, Han L, Ma H, Yang H, Han C, Lu X. PRMT6 Epigenetically Drives Metabolic Switch from Fatty Acid Oxidation toward Glycolysis and Promotes Osteoclast Differentiation During Osteoporosis. Adv Sci (Weinh). 2024 Oct;11(40):e2403177. doi: 10.1002/advs.202403177. Epub 2024 Aug 9. PMID: 39120025; PMCID: PMC11516099. Chen C, Chen B, Lin Y, He Q, Yang J, Xiao J, Pan Z, Li S, Li M, Wang F, Zhang H, Wang X, Zeng J, Chi W, Meng K, Wang H, Chen P. Cardamonin attenuates iron overload-induced osteoblast oxidative stress through the HIF-1α/ROS pathway. Int Immunopharmacol. 2024 Dec 5;142(Pt A):112893. doi: 10.1016/j.intimp.2024.112893. Epub 2024 Aug 31. PMID: 39217878. Huang G, Yin W, Zhao X, Xu M, Wang P, Li R, Zhou L, Tang W, Jiao J. Osteoking inhibits apoptosis of BMSCs in osteoporotic rats via PI3K/AKT signaling pathway. J Ethnopharmacol. 2025 Jan 31;340:118961. doi: 10.1016/j.jep.2024.118961. Epub 2024 Dec 7. PMID: 39653105. Li H, Gong Y, Wang Y, Sang W, Wang C, Zhang Y, Zhang H, Liu P, Liu M, Sun H. β-Sitosterol modulates osteogenic and adipogenic balance in BMSCs to suppress osteoporosis via regulating mTOR-IMP1-Adipoq axis. Phytomedicine. 2025 Mar;138:156400. doi: 10.1016/j.phymed.2025.156400. Epub 2025 Jan 17. PMID: 39848018. Bandara N, Gurusinghe S, Chen H, Chen S, Wang LX, Lim SY, Strappe P. Minicircle DNA-mediated endothelial nitric oxide synthase gene transfer enhances angiogenic responses of bone marrow-derived mesenchymal stem cells. Stem Cell Res Ther. 2016 Apr 1;7:48. doi: 10.1186/s13287-016-0307-2. PMID: 27036881; PMCID: PMC4818467. Takada I, Kouzmenko AP, Kato S. Molecular switching of osteoblastogenesis versus adipogenesis: implications for targeted therapies. Expert Opin Ther Targets. 2009 May;13(5):593-603. doi: 10.1517/14728220902915310. PMID: 19397478. Komori, T., Yagi, H., Nomura, S., et al. (1997). Targeted disruption of Cbfa1 results in a complete lack of bone formation owing to maturational arrest of osteoblasts. Cell, 89(5), 755-764. Arafa EA, Elgendy NO, Elhemely MA, Abdelaleem EA, Mohamed WR. Diosmin mitigates dexamethasone-induced osteoporosis in vivo: Role of Runx2, RANKL/OPG, and oxidative stress. Biomed Pharmacother. 2023 May;161:114461. doi: 10.1016/j.biopha.2023.114461. Epub 2023 Mar 6. PMID: 36889109. Additional Declarations No competing interests reported. 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2","display":"","copyAsset":false,"role":"figure","size":178004,"visible":true,"origin":"","legend":"\u003cp\u003eZGW UPLC-MS/MS chromatograms\u003c/p\u003e\n\u003cp\u003eA, B: total ion chromatograms of blank positive (A) and negative (B) ion modes, C, D: total ion chromatograms of ZGW tonics positive (C) and negative (D) ion modes, E, F: total ion chromatograms of control serum positive (E) and negative (F) ion modes, G, H: total ion chromatograms of ZGW containing serum positive (G) and negative (H) ion modes.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/c0cb80969046c0c669c2c4fa.png"},{"id":95821317,"identity":"ee365d20-db39-4a87-a3c7-21472e2679d1","added_by":"auto","created_at":"2025-11-13 10:47:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":958865,"visible":true,"origin":"","legend":"\u003cp\u003eThe Effect of ZGW on Bone Mineral Density and Bone Morphometry in Ovariectomized Rats\u003cbr\u003e\n(A) Micro-CT images of the femur.(B) Trabecular thickness (Tb.Th).(C) Trabecular bone density (Tb.Sp).(D) Trabecular number (Tb.N).(E) Structure Model Index (SMI).(F) Bone volume fraction (BV/TV).(G) Bone mineral density (BMD).Data are expressed as mean ± standard error (n = 3). ***P \u0026lt; 0.001, **P \u0026lt; 0.01, *P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/5d89b552347addfe4b895095.png"},{"id":95821298,"identity":"cd4af68d-9af1-4a40-802e-2381453a0526","added_by":"auto","created_at":"2025-11-13 10:47:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":949813,"visible":true,"origin":"","legend":"\u003cp\u003eThe Effect of ZGW on Bone Tissue Pathology and Serum Bone Metabolic Markers in Ovariectomized Rats\u003cbr\u003e\n(A) H\u0026amp;E stained bone tissue sections from each group. Scale bar = 500 μm; inset scale bar = 100 μm.Serum bone metabolic marker expression levels include:(B) BALP,(C) CTX-I,(D) E2,(E) BGP.Data are expressed as mean ± standard error (n = 3). Comparison with OVX: ***P \u0026lt; 0.001, **P \u0026lt; 0.01, *P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/ad52dcbfd5918b6e95571400.png"},{"id":95821300,"identity":"7e1705d4-b354-425d-a14f-cd46cf5ef9e5","added_by":"auto","created_at":"2025-11-13 10:47:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1093334,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork of ZGW targets for PMOP\u003c/p\u003e\n\u003cp\u003eA: PMOP-related targets B: Targets at the intersection of ZGW and PMOP C: botanical drug-metabolite-target-disease network of ZWG for PMOP\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/c9208e1bc577e3c9e67ae306.png"},{"id":96238951,"identity":"d7d86aef-0158-4e89-8231-0eead9c8bb10","added_by":"auto","created_at":"2025-11-19 06:57:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":567399,"visible":true,"origin":"","legend":"\u003cp\u003eZWG treatment of PMOP core targets\u003c/p\u003e\n\u003cp\u003eA:PPI network of ZWG and PMOP cross-targets B:Sankey diagram consisting of 5 metabolites and 10 core targets\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/546d6551166fae7fce25ce37.png"},{"id":95821307,"identity":"c3b343e1-1233-4f4e-9e17-be8773f2f68d","added_by":"auto","created_at":"2025-11-13 10:47:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":279701,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG enrichment analysis of ZGW in the treatment of PMOP.\u003c/p\u003e\n\u003cp\u003eA:Biological process (BP),B:Cellular metabolite (CC),C:Molecular function (MF),D:KEGG pathway analysis.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/319ac84aae47d4ccd3dc6181.png"},{"id":95821295,"identity":"b7db40a3-d6cf-4025-9669-ca0f5521eb03","added_by":"auto","created_at":"2025-11-13 10:47:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":603422,"visible":true,"origin":"","legend":"\u003cp\u003e3D plot of the molecular docking mode:3,4-dihydroxybenzoic acid together with NFKB1 (A),MMP9 (B) and HIF1 (C).Dl-tyrosine together with STAT3 (D),PTGS2 (E), NFKB1 (F).\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/f65ed409812d2326b9e8b273.png"},{"id":95821312,"identity":"999503e1-427a-4246-b764-40f67c3f67b3","added_by":"auto","created_at":"2025-11-13 10:47:14","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":712538,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of ZGW-containing serum on osteogenic differentiation of MC3T3-E1 cells\u003c/p\u003e\n\u003cp\u003eA: Viability rate of MTT cells, B: Effect of ZGW-containing serum on alkaline phosphatase activity of MC3T3-E1 cells C: Alizarin red S staining in 20% control serum group, D: Alizarin red S staining in 20% serum-containing serum group, E: Quantitative analysis of alizarin red S staining; Comparison of the corresponding time to that of the control serum group: *p\u0026lt;0.05.,**p\u0026lt;0.01 , ****p\u0026lt;0.0001; Comparison with the same serum group at different times: #p\u0026lt;0.05\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/2e6167dec60670c8b85d1179.png"},{"id":95821331,"identity":"191f7c46-20a9-41bd-9b1e-35e931fb644b","added_by":"auto","created_at":"2025-11-13 10:47:21","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":170501,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of ZGW-containing serum on relevant mRNAs\u003c/p\u003e\n\u003cp\u003eA: Effect of ZGW-containing serum on core target mRNAs B: Effect of ZGW-containing serum on PI3K-related pathway C: Effect of ZGW-containing serum on osteogenic marker proteins;compared to NC:, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/2aeb5d12fef910d4fbbbe5c0.png"},{"id":95821296,"identity":"c0227cf5-5fcb-47d6-9c78-efeb1d9d7cf4","added_by":"auto","created_at":"2025-11-13 10:47:11","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":604098,"visible":true,"origin":"","legend":"\u003cp\u003eThe Effect of ZGW Medicated Serum on Relevant Proteins\u003cbr\u003e\n(A) The effect of ZGW medicated serum on core target proteins in MC3T3-E1 cells.(B) The effect of ZGW medicated serum on osteogenic marker proteins in MC3T3-E1 cells.(C) Quantitative analysis of relevant proteins in MC3T3-E1 cells treated with ZGW medicated serum.(D) The effect of ZGW medicated serum on relevant proteins in BMSCS cells.(E) Quantitative analysis of relevant proteins in BMSCS cells treated with ZGW medicated serum.Compared with NC, p \u0026gt; 0.05, *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001,\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/98e68f7fe1270200a200bee4.png"},{"id":95821305,"identity":"fc9e3827-29ff-4b46-a743-dbe8a9baed89","added_by":"auto","created_at":"2025-11-13 10:47:14","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":384021,"visible":true,"origin":"","legend":"\u003cp\u003eZGW Promotes Osteogenesis via the PI3K and HIF Pathways\u003cbr\u003e\n(A) Verification of inhibitor effectiveness.(B) The effect of the PI3K and HIF pathways on the osteogenic effect of ZGW.(C-F) Quantitative analysis.Compared with NC, p \u0026gt; 0.05, *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/1e2fb5de49dd8aa168f1a887.png"},{"id":96254367,"identity":"58918a6c-9fa7-4369-b60e-867e6ddfad86","added_by":"auto","created_at":"2025-11-19 07:46:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8363926,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/5f57ab13-3c2c-4562-9d95-a63d189d8045.pdf"},{"id":95821107,"identity":"2bd171c6-3a77-4522-84b4-045195e8742f","added_by":"auto","created_at":"2025-11-13 10:47:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22209,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1TableS1Drug.docx","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/2e43d1e1c776d7753c82c813.docx"},{"id":95821250,"identity":"8f3767c0-7f09-40c5-83c5-e2e0496d8267","added_by":"auto","created_at":"2025-11-13 10:47:07","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26755,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary2TCMNegativeIonIdentificationResults.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/22ef4985dd68e9ac34dad1b0.xlsx"},{"id":95821053,"identity":"a4e98b2f-558d-4bc1-b524-b6febc9a6d0c","added_by":"auto","created_at":"2025-11-13 10:47:01","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":58019,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary3TCMpositiveionidentificationresults.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8011125/v1/1f27855a8049b81d30dd82b5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Zuo Gui Wan Restores Bone Metabolism in Postmenopausal Osteoporosis through HIF-1 and PI3K–Akt Pathway Modulation: Evidence from UPLC– MS/MS and Network Analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePMOP represents a significant public health issue, characterized by a systemic decline in bone metabolism due to reduced estrogen levels[\u003csup\u003e1]\u003c/sup\u003e, leading to decreased bone mass and deteriorated trabecular architecture. This results in bones that are fragile and susceptible to fractures[\u003csup\u003e2]\u003c/sup\u003e. It is estimated that 35.3% of elderly women worldwide are affected by PMOP[\u003csup\u003e3]\u003c/sup\u003e, with substantial incidence rates in the UK where half of the women sustain at least one fragility fracture, as opposed to a fifth of men[\u003csup\u003e4]\u003c/sup\u003e. Given these statistics, effective treatment options are essential not only for enhancing the quality of life of patients but also for lessening societal healthcare burdens. Historically, hormone replacement therapy (HRT) and bisphosphonates have been the mainstays of treatment, yet they are not devoid of drawbacks. For instance, extended administration of HRT can result in negative outcomes such as breast cancer and cardiovascular conditions, whereas bisphosphonates are associated with serious complications like osteonecrosis of the jaw[\u003csup\u003e5]\u003c/sup\u003e. Consequently, there is an increasing demand for safe and effective alternative therapies.\u003c/p\u003e\n\u003cp\u003eTraditional Chinese medicine (TCM) has been employed for centuries to manage bone ailments. ZGW, originating from \u0026ldquo;Jingyue Quanshu\u0026middot;New Prescriptions Eight Arrays\u0026rdquo; by Zhangjiebin, comprises eight botanical drugs: Cuscuta chinensis Lam. (Tu Sizi), Dioscorea oppositifolia L. (Shan Yao), Lycium barbarum L. (Gou Qi), Cervi Cornus Colla (Lu Jiaojiao), Rehmannia glutinosa (Gaertn.) DC (Shu Di), Cornus officinalis Siebold \u0026amp; Zucc. (Shan Zhuyu), Testudinis Carapacis Et Plastri Colla (Gui Banjiao), and Cyathula officinalis K.C.Kuan (Niu Xi). Verified against the MPNS database, these metabolites are traditionally used to alleviate symptoms such as lumbar pain, leg weakness, and symptoms arising from yin deficiency. Specifically, Rehmanniae Radix Praeparata is noted for its antioxidative and endocrine regulatory functions; Dioscoreae Rhizoma is recognized for its anti-aging properties and immune-enhancing effects[\u003csup\u003e6]\u003c/sup\u003e; polysaccharides in Lycii Fructus exhibit anti-inflammatory and anti-apoptotic properties[\u003csup\u003e7]\u003c/sup\u003e; and Corni Fructus influences bone metabolism-related signaling pathways[\u003csup\u003e8]\u003c/sup\u003e. Collectively, these effects synergistically combat the negative impact of estrogen deficiency, inhibiting bone resorption and fostering bone formation, thereby aiding in the prevention and treatment of PMOP. Research indicates that ZGW impacts the progression of osteoporosis through the RANKL/OPG pathway[\u003csup\u003e9]\u003c/sup\u003e and helps restore bone mass lost due to diminished estrogen levels. However, despite its demonstrated efficacy, the clinical use of ZGW is mired in controversies. Firstly, the complexity of its herbal metabolites and the ambiguity of its active metabolites challenge its standardization. Secondly, the intricacies of its production process call for rigorous safety assessments. This study, therefore, integrates UPLC-MS/MS with network analysis and experimental validations to elucidate the effective metabolites and mechanisms by which ZGW combats PMOP and to confirm its safety(Fig.1), offering new theoretical and empirical support for the application of TCM in treating this condition.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cp\u003e2.1 Materials and reagents\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZuo Gui Wan:24g of Rehmanniae Radix Praeparata, 12g of Dioscoreae Rhizoma, Lycii Fructus, Corni Fructus, Cuscutae Semen, Cervi Cornus Colla, Testudinis Carapacis Et Plastri Colla, and 9g of Cyathulae Radixwere\u0026nbsp;all provided by the pharmacy of Wangjing Hospital, China Academy of Chinese Medical Sciences. All samples were kept in the Medical Experimental Centre of China Academy of Traditional Chinese Medicine. The following experimental equipment was used: vertical electrophoresis (Servicebio, model BV-2); microscope (Nikon, model E100); enzyme labelling instrument (BioTeK, model Epoch); desktop high-speed refrigerated microcentrifuge (DragonLab, model D3024R); fluorescence quantitative PCR instrument (Bio-rad, CFX Connect model). The following experimental reagents were used: MTT assay kit (Wuhan Xavier Biotechnology Co., Ltd., No. G4101); BCA protein quantitative detection kit (Wuhan Xavier Biotechnology Co., Ltd., No. G2026-200T); alkaline phosphatase assay kit (Nanjing Jianjian Bioengineering Research Institute, No. A0592);\u0026nbsp;\u0026alpha;-MEM liquid culture medium ( HyClone, No. SH30265.01); Australian fetal bovine serum (Gibco, No. 10099-141); penicillin-streptomycin solution (HyClone, No. SV30010); 0.25% trypsin (Gibco, No. SH30042.01); osteogenic inducing agents (Elabscience Biotechnology Co. Ltd, No. PD-033); SweScript All-in-One RT SuperMix for qPCR (One-Step gDNA Remover) (Wuhan Xavier Biotechnology Co. Ltd, No. G3337); 2\u0026times;Universal Blue SYBR Green qPCR Master Mix (Wuhan Xavier Biotechnology Co., Ltd., No. G3326).\u003c/p\u003e\n\u003cp\u003e2.2 Animals and Cells\u003c/p\u003e\n\u003cp\u003e62 female SPF-grade SD rats, aged 8 weeks and weighing approximately 200\u0026plusmn;20 grams, were obtained using an animal production license (SCXK (Beijing) 2019-0010) held by Sipf Bio-Tech Co., Ltd. These rats were housed at a facility in the Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences. The environmental conditions were controlled with a 12-hour light/dark cycle, humidity levels of 40% to 50%, and temperatures between 20\u0026deg;C and 25\u0026deg;C. MC3T3-E1 cells were sourced from Saibekang Biotechnology Co., Ltd. (Shanghai, China) (Catalog number iCell-m031). BMSCS cells were obtained from Haixing Biotechnology Co., Ltd. (Fujian, China) (Catalog number BMRS-C106I).\u003cbr\u003e\u0026nbsp;The animal study described in this research was ethically approved by\u0026nbsp;the Ethics Center of the Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences. (Approval No. 2025B036), in compliance with the guidelines outlined in the 1986 EEC Directive (86/609/EEC).\u003c/p\u003e\n\u003cp\u003e2.3 Preparation of Drug-Containing Serum\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe ZGW formula contains several herbal metabolites, including 24g of Rehmannia, 12g of Sanguisorba, 12g of Lycium, 12g of Cornus, 12g of Cuscuta, and 9g of Cornus. These botanical drugs were soaked for 1 hour, followed by decoction for 1 hour. Afterward, they were soaked for 40 minutes, and then decocted again for 1 hour to obtain the second batch. Both decoctions were combined and concentrated to form a Chinese medicine solution with a density of 1.1025g/ml.\u003c/p\u003e\n\u003cp\u003e20 SD rats were divided into two groups: a control group and a treatment group, with 10 rats in each group. Based on the human equivalent dose (calculated using a standard body weight of 60 kg), the treatment group received a ZGW solution at a concentration of 1.1025g/ml, administered once daily at a dose of 1 ml/ (100g\u0026middot;d) for 7 days. The control group was administered the same volume of saline solution. One hour after the final dose, the rats were sedated and blood samples were collected via the inferior vena cava. The blood was kept at 4\u0026deg;C for 3 hours, then centrifuged at 3000 rpm for 15 minutes (centrifugal radius of 14 cm) at 4\u0026deg;C. The serum was inactivated in a 56\u0026deg;C water bath for 30 minutes and stored at -80\u0026deg;C for subsequent analysis.\u003c/p\u003e\n\u003ch4\u003e2.4 UPLC-MS/MS Analysis\u003c/h4\u003e\n\u003cp\u003eFor the analysis of ZGW active metabolites, UPLC-MS/MS was employed to examine the prototype drug along with blank and medicated serum samples from rats. The chromatographic examination was carried out on 100 \u0026mu;L of each sample type using a Waters UPLC HSS T3 column (1.8 \u0026mu;m, 2.1 mm \u0026times; 100 mm). The chromatographic separation was carried out using a gradient elution approach, as shown in Table 1. The mobile phase consisted of solvent B (methanol) and solvent A (water + 0.1% formic acid). The column temperature was kept at 40\u0026deg;C, the injection volume was 10.0 \u0026mu;l, and the flow rate was set at 0.3 ml/min.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn electrospray ionization source was utilized in the mass spectrometry analyses performed utilizing a Q\u0026nbsp;Exactive\u0026trade;\u0026nbsp;quadrupole-orbitrap ion trap mass spectrometer. The device was set up with a positive ion source voltage of 3.7 kV and a negative ion source voltage of 3.5 kV. With 30 psi for the sheath and 10 psi for the auxiliary gas, the capillary temperature was fixed at 320\u0026deg;C. With nitrogen acting as both a sheath and collision gas, the temperature of solvent evaporation was maintained at 300\u0026deg;C, with the latter operating at a pressure of 1.5 mTorr. A resolution of 70000, an automated gain control (AGC) target of\u0026nbsp;1\u0026times;10\u003csup\u003e6\u003c/sup\u003e, a maximum ion isolation duration of 50 ms, and a mass-to-charge ratio scan range of 100-1500 were all part of the full scan parameters. Mass spectrometer calibration utilized external standards to maintain a mass error within 5 ppm, including calibration ions for both positive (74.09643, 83.06037, 195.08465, 262.63612, 524.26496, 1022.00341) and negative (91.00368, 96.96010, 112.98559, 265.14790, 514.28440, 1080.00999) modes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing parameters like a resolution of 17500, an AGC goal of 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e, and a maximum ion isolation duration of 50 ms, metabolicate identification made use of the dd-MS2 scanning mode, which is data-dependent scanning mode. Up to 10 secondary ion fragments were examined per scan with a dynamic exclusion, mass separation window of 2, collision energy set at 30 V, and an intensity threshold of 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e. Data acquisition and system control were facilitated by Xcalibur software version 2.2 SP1.48.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 Elution gradient\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"457\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003etime (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003emobile phase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026nbsp;（v%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eB\u0026nbsp;（v%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e50.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e2.5 In vivo experiment\u003c/p\u003e\n\u003cp\u003e2.5.1Animal Model and Grouping:\u003c/p\u003e\n\u003cp\u003eTo establish an ovariectomized rat model of osteoporosis, the experiment was conducted in accordance with the National Regulations for Animal Experimentation. The PMOP rat model induced by OVX was established following methods described in previous literature\u0026nbsp;[\u003csup\u003e10]\u003c/sup\u003e. The procedure is as follows: After anesthetizing the rats with a 2% sodium pentobarbital solution (40 mg/kg), the rats were placed in a prone position, and a 2 cm long incision was made along the midline of the back. Muscle tissue was carefully separated to locate and sequentially excise the bilateral ovaries. The incision was then sutured layer by layer and disinfected with iodine tincture.\u0026nbsp;After surgery, rats were intraperitoneally injected with penicillin sodium solution (80,000 units) for three consecutive days to prevent infection.A total of forty-two Sprague\u0026ndash;Dawley (SD) rats were randomly divided into seven groups (n = 6 per group): normal control (NC), sham-operated (SHAM), ovariectomized model (OVX), low-dose Zuo Gui Wan (ZGW-L), medium-dose Zuo Gui Wan (ZGW-M), high-dose Zuo Gui Wan (ZGW-H), and positive control with alendronate sodium (ALN). Rats in the NC group were maintained under standard feeding conditions and received 0.9% saline by gavage. In the SHAM group, a subcutaneous incision was made and sutured without further procedures, followed by gavage with 0.9% saline. The OVX group underwent bilateral ovariectomy to induce osteoporosis. The ZGW-L, ZGW-M, and ZGW-H groups received ZGW extract at concentrations of 0.5513 g/mL, 1.1025 g/mL, and 2.205 g/mL, respectively, at a dose of 1 mL/100 g body weight once daily. The ALN group received alendronate sodium enteric-coated tablets at 6.3 mg/kg/week by gavage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll treatments lasted for 12 weeks. After the final dose, 24 hours later, the rats were anesthetized with intraperitoneal injection of sodium pentobarbital (3%, 0.15mL/100g). The left tibia was excised and soaked in 4% paraformaldehyde solution for hematoxylin-eosin (HE) staining. The right tibia was preserved at \u0026minus;80\u0026deg;C for Western blot analysis. The right femur was wrapped with moist gauze for biomechanical testing, and the left femur was immersed in 4% paraformaldehyde solution for micro-CT scanning.\u003c/p\u003e\n\u003cp\u003e2.5.2 Micro-Computed Tomography (Micro-CT):\u003cbr\u003e\u0026nbsp;Micro-CT scanning of the femur was performed using a Skyscan 1276 micro-CT (Bruker, USA), with a voltage of 70 kV, current of 200 \u0026mu;A, and a scanning resolution of 10.2 \u0026mu;m, with a field of view of 2016\u0026times;1344. The bone marrow cavity of the distal femur, 3mm below the growth plate, was defined as the region of interest (ROI), where trabecular bone morphology parameters and bone mineral density (BMD) were measured. The primary analysis indicators included: bone mineral density (BMD) (g/cm\u0026sup3;), trabecular thickness (Tb.Th) (mm), bone volume fraction (BV/TV) (%), trabecular number (Tb.N) (1/mm), trabecular separation (Tb.Sp) (mm), and Structure Model Index (SMI).\u003c/p\u003e\n\u003cp\u003e2.5.3 H\u0026amp;E Staining of Tibial Bone Tissue:\u003cbr\u003e\u0026nbsp;The proximal tibial tissue was decalcified in 10% EDTA solution for 1 month, with the solution being changed weekly. The hardness of the tissue was monitored throughout the decalcification process. After decalcification, femur sections (5 \u0026mu;m thickness) were dewaxed and rehydrated, then stained with hematoxylin and eosin for 5 minutes, followed by dehydration, clearing, and mounting. All sections were examined under a microscope to record the effects of staining on tissue structure.\u003c/p\u003e\n\u003cp\u003e2.5.4 Enzyme-Linked Immunosorbent Assay (ELISA) Detection:\u003cbr\u003e Serum samples from rats were used to detect the levels of estradiol (E2), bone-specific alkaline phosphatase (BALP), type I collagen C-terminal cross-linking telopeptide (CTX-1), and osteocalcin (BGP) using commercial ELISA kits according to the manufacturer\u0026rsquo;s instructions. The ELISA kits were purchased from Enzyme-linked Biotech Co., Ltd. (Shanghai, China).\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e2.6 network analysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Analysis\u003c/strong\u003e\u003c/h4\u003e\n\u003ch4\u003e\u003cstrong\u003e2.6.1 Identification of ZGW metabolites and Action Target Prediction\u003c/strong\u003e\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eBy utilizing ultra-performance liquid chromatography linked to a quadrupole orbitrap mass spectrometer (UPLC-Q-Orbitrap-MS), the metabolites of the TCM formulation ZGW were described. This process included the analysis of both in vitro chemical metabolites and in vivo metabolic products. Information regarding individual herbal metabolites was collated from various commercial and public databases such as UNIFI, HERB, TCMSP, and ETCM, supplemented by existing scientific literature. After compiling this data and removing duplicates, a comprehensive database of single-herb chemical metabolites specific to ZGW was created. Crucial metabolites were pinpointed based on the Chinese Pharmacopoeia and pertinent analytical literature. A multi-dimensional analysis was carried out considering factors like parent ion mass accuracy, match of secondary fragments, isotopic distribution, and peak intensities to confirm the identified metabolites. These metabolites were then validated, and their Isomeric and Canonical SMILES were retrieved from PubChem. Prediction of the biological targets for these metabolites was performed using tools like Swiss Target Prediction and Super-PRED, retaining targets with probabilities greater than zero in Swiss Target Prediction and 60% or higher in Super-PRED, followed by deduplication.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e2.6.2 Collection of PMOP Targets\u003c/strong\u003e\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eTargets associated with PMOP were sourced from several prominent databases, including DrugBank[\u003csup\u003e11]\u003c/sup\u003e, GeneCards[\u003csup\u003e12]\u003c/sup\u003e, TTD[\u003csup\u003e13]\u003c/sup\u003e, DisGeNET[\u003csup\u003e14]\u003c/sup\u003e, and OMIM[\u003csup\u003e15]\u003c/sup\u003e. These resources provided a comprehensive list of potential PMOP-related targets. The data from these platforms were integrated and duplicates were removed. Additionally, the most significant 1000 targets were identified from the Gene Expression Omnibus (GEO) using an analysis of GSE230665.top.table (1) with geo2r, focusing on those with the lowest p-values.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e2.6.3 Construction of the \u0026quot;botanical drug-metabolite-Target-Disease\u0026quot; Network\u003c/strong\u003e\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eWe used Cytoscape 3.8.0 to build the \u0026quot;botanical drug-metabolite-target-disease\u0026quot; network, which combines the anticipated targets from ZGW with the gathered PMOP-related targets. Within this network, active metabolites were identified based on their Node Betweenness, utilizing the CytoNCA plugin, highlighting those with significant influence across the network.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e2.6.4 PPI Analysis of Intersecting Targets\u003c/strong\u003e\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eIntersecting targets from ZGW and PMOP were analyzed through the STRING database, selecting interactions with a confidence score above 0.4. The resulting interaction data were exported as a TSV file. Subsequently, details regarding node1, node2, and the combined score were imported into Cytoscape to establish the PPI network. Hub genes within this network were determined using the CytoNCA tool. Additionally, Sankey diagrams depicting the relationships between drugs, core metabolites, and key genes were created.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e2.6.5 Enrichment Analysis\u003c/strong\u003e\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eEnrichment analyses for GO and KEGG pathways of the intersecting targets were conducted utilizing the \u0026ldquo;clusterProfiler [4.4.4]\u0026rdquo; package. Results from these analyses were visualized with the \u0026ldquo;ggplot2 [3.3.6]\u0026rdquo; package, and corresponding bubble charts were generated.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e2.6.6 Molecular Docking\u003c/strong\u003e\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eAfter retrieving the three-dimensional (3D) structural data of important metabolites from PubChem in SDF format, we used Open Babel to convert them to PDB format. In order to create clean PDB files, the 3D crystal structures of key genes were obtained from the Protein Data Bank (PDB) and, using PyMol 2.4.0, any unnecessary ions or water molecules were eliminated. The next step in identifying active binding sites was to convert these metabolites and target proteins to PDBQT format. To visualize the docking data, we used PyMol 2.4.0 and ran the molecular docking simulations with Autodock Vina.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Experimental programme\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCell Culture and Treatment:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;MC3T3-E1 cells were cultured in \u0026alpha;-MEM base medium, while BMSCS cells were cultured in DMEM base medium. The efficacy experiments included a blank serum group and a medicated serum group, with different serum concentrations used for interventions. In the network analysis mechanism verification, in addition to the blank group (NC) and the Zuo Gui Wan (ZGW) medicated serum group, two additional groups were included: the MC3T3-E1 medicated group with the addition of LY294002 (PI3K/AKT inhibitor) (ZGW+LY), and the MC3T3-E1 medicated group with both LY294002 and DMOG (HIF pathway activator) (ZGW+LY+DMOG).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSafety and Cell Proliferation Assay of ZGW\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP3 MC3T3-E1 cells were plated in 96-well plates at 1\u0026times;105 cells/ml, 100 \u0026mu;l per well. After incubating for 24 hours, the cells were organized into various groups receiving either control serum or drug-containing serum at concentrations of 10%, 15%, 20%, and 25%. Three times were each treatment condition repeated. Additional 24, 48, and 72 hour incubation durations were subsequently applied to the cells. Then, 50 \u0026mu;L of 1\u0026times; MTT reagent was added to every well and left to incubate for four hours. Each well was then treated with 150 \u0026mu;L of DMSO after the culture media was removed, and the mixture was incubated for 10 minutes with gentle shaking. To find out how viable each group\u0026apos;s cells were, a microplate reader measured their optical density (OD) at 570 nm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7.3 Detection of cellular alkaline phosphatase activity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cellular alkaline phosphatase activity was detected by microplate assay, according to the cell concentration of 1\u0026times;105/mL, the 3rd generation MC3T3-E1 cells were inoculated in 96-well plates, each well was cultured with 100 \u0026mu;L \u0026alpha;-MEM complete medium, and after 24 h of inoculation, the cells were divided into 20%, control serum group, and the corresponding volume fraction of the drug-containing serum group, and corresponding serum interventions were carried out respectively, and each group was set up with three metabolite wells, incubated in the incubator for 48 h, the upper layer of the cell culture medium was taken, and the alkaline phosphatase activity in the culture medium was detected according to the instructions of the kit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDetection of osteogenic activity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlizarin red S staining was used to detect the osteogenic activity of the cells, and the 3rd generation MC3T3-E1 cells were inoculated into 24-well plates according to the cell concentration of 1\u0026times;105/mL, and each well was cultured with 400 \u0026mu;L of \u0026alpha;-MEM complete medium, and after 24 h of inoculation, the cells were divided into two groups: a 20% control serum group and a 20% drug-containing serum group. Each group was treated with the corresponding serum. Additionally, an equal volume of 1% osteogenic differentiation induction medium was added to each group. Alizarin Red S staining was performed on day 20. Firstly, the cell culture solution of each group was thoroughly aspirated, and the cells were washed twice with PBS, 400 \u0026micro;L of 40 g/L neutral formaldehyde solution was added to each well, and the formaldehyde solution was aspirated after fixation at room temperature for 15 min, the cells were washed twice with PBS, 400 \u0026micro;L of alizarin red S staining solution was added along the edges of the wells, and the cells were incubated at room temperature and protected from light for 15 min, then the alizarin red S staining solution was aspirated and washed three times with PBS in order to reduce the background staining, and finally the calcification of each group of cells was observed by microscope, and the staining results were photographed and recorded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7.5 Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Trizol reagent was used to extract total RNA from every single cell type. One way to measure the concentration and purity of RNA is with a micro-spectrophotometer. After these measurements, a 20\u0026mu;L reaction volume of mRNA was transformed into cDNA using a Takara reverse transcription kit. Table 2 details the primer sequences and PCR conditions. The following settings were used to run the qRT-PCR: denaturation at 95\u0026deg;C for 30 seconds for one cycle, PCR amplification at 95\u0026deg;C for 5 s and 60\u0026deg;C for 30 s for 40 cycles, and finally, a melting curve analysis with 30 s at 60\u0026deg;C, 1 minute at 95\u0026deg;C, and 15 s at 95\u0026deg;C. The analytic program was used to extract data, utilizing GAPDH as the reference gene. Triplicates of each experimental condition were conducted. We used the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;CT\u0026nbsp;\u003c/sup\u003emethod to quantify gene expression, and then we used statistical analysis and bar graphs to show the results.\u003c/p\u003e\n\u003cp\u003eTable 2. Sequences of PCR primers\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGene symbol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAccession number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eForward primer (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReverse primer (5\u0026rsquo;-3\u0026rsquo;）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAmplicon size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMMP9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_013599.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCTCGGGAAGGCTCTGCTGTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAACTCACACGCCAGAAGAATTTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eESR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_001302531.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCAGGCTTTGGGGACTTGAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGAGCAAGTTAGGAGCAAACAGGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_009654.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCGCTACACCCAGAAAGCACCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eACGGTTCAGGATTGCAGACAGATA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_007912.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCCGAAACTACGTGGTGACAGAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTGCCATTACAAACTTTGCGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNFKB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_001410442.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGAGTCACGAAATCCAACGCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCGTCATCACTCTTGGCACAATC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTLR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_021297.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTGAGGACTGGGTGAGAAATGAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCTGCCATGTTTGAGCAATCTCAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSTAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_011486.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTGCGGAGAAGCATTGTGAGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTCTTAATTTGTTGGCGGGTCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHIF1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_001313919.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTTGCTTTGATGTGGATAGCGATA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCATACTTGGAGGGCTTGGAGAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMAPK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_053842.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAACCTCCTGCTGAACACCACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCGTGGCTACATACTCTGTCAAGAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePIK3CD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_001029837.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTCCTTCGCCATCAAGTCCCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAGAGCGGAGGTGCCAGAACA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePIK3R1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_001024955.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTTGACAGTAGGAGGAGGTTGGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCAGGGAGTATTGATCTTCGGTATT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePIK3CB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_029094.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGTGCTAATGTGTCAAGTCGTGGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCAGTCTTGCCGTAGAGTCCAAATAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNOS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_008713.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCAATCTTCGTTCAGCCATCACAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGGAGCCATCCTGCTGCCTAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePIK3CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_008839.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eATGGAGGAGAACCCTTATGTGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAGATTGAAAGGCAAAGGCGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSERPINE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_008871.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGGCCTCCAAAGACCGGAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eACAAAGATGGCATCCGCAGTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMTOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_020009.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCCTTCACAGATACCCAGTACCTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAGTAGACCTTAAACTCCGACCTCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOSTERIX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_001348205.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTCTGCGGCAAGAGGTTCACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGCTGATGTTTGCTCAAGTGGTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRUNX2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_001145920.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eATGACACTGCCACCTCTGACTTCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAGGGATGAAATGCTTGGGAACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_008084.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCCTCGTCCCGTAGACAAAATG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTGAGGTCAATGAAGGGGTCGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNM_012870.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAATTGTGGAATAGATGTCACCCTGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCAAACTGTCCACCAGAACACTCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e2.7.6\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eWestern Blot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein immunoblotting was used to detect the detection of protein expression of MC3T3-E1 cells in each group. According to the cell concentration of 1\u0026times;105/mL, the 3rd generation MC3T3-E1 cells were inoculated in 10 cm dishes with 10 ml each and incubated for 24 h. 20% by volume\u0026nbsp;control\u0026nbsp;serum and 20% by volume Zuoguiwan drug-containing serum were given to intervene for 48 h. After treatment, the MC3T3-E1 cells were first washed twice with pre - chilled PBS solution (4℃, 2 mL in volume), and then RIPA lysis solution was added to achieve cell lysis. After lysis was completed, cell supernatants were separated and collected by centrifugation at 14,000 rpm for 10 min at 4 degrees Celsius in order to extract total proteins, and the total protein concentration of each group was determined by BCA method. The protein samples were mixed well with 5\u0026times; protein uploading buffer, and then heated in a metal bath at 99 ℃ for 10 min to denature the proteins completely. For electrophoresis, the amount of protein calculated after the BCA method was applied, and the proteins were transferred to a polyvinylidene difluoride membrane using electrotransfer technology, and the transferred membrane was closed with a sealing solution for 1 h. After that, the appropriately diluted primary antibody anti-GAPDH antibody（abcam，1：15000，ab181602），anti-NFKB antibody（proteintech，1：1000，15506-1-AP），anti-EGFR antibody（proteintech，1：3000，18986-1-AP），anti-Albumin antibody（proteintech，1：30000，16475-1-AP），anti-STAT3 antibody（proteintech，1：3000，10253-2-AP）anti-MMP9 antibody（proteintech，1：1000，27306-1-AP），anti-HIF1a antibody（proteintech，1：5000，80933-1-RR），anti-TLR4 antibody（proteintech，1：2000，19811-1-AP）were incubated at 4 \u0026deg;C cold storage on a shaker overnight for incubation. On the following day, the antibody was washed three times with TBST buffer for 5 min each time for a continuous period of time, HRP-labelled secondary antibody was added at a dilution of 1:1000, and the antibody was washed three times for 5 min each time after incubation for 1 h at room temperature, and finally, the colour reaction was carried out by using ultrasensitive ECL chemiluminescent reagent, and the images of the bands were captured by a gel-imaging system. The bands were analysed using ImageLab 5.2 software, and the relative ratio of the grey value of the target protein to that of the internal reference protein GAPDH was calculated as the corrected protein expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor statistical evaluation of the experimental results, we used GraphPad Prism version 9.0. t-tests were used to compare differences between two groups, while one-way analysis of variance (ANOVA) was used for analyses involving multiple groups. Data are reported as mean \u0026plusmn; standard deviation (SD). Statistical significance was established at a threshold of p\u0026lt;0.05. Significance levels are annotated as follows: ns for p\u0026gt;0.05, * for p\u0026lt;0.05, ** for p\u0026lt;0.01, *** for p\u0026lt;0.001, and *** for p\u0026lt;0.0001.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 ZGW\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emetabolite\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the chromatographic conditions, positive and negative ionograms were collected from the control mode (Fig. 2A, B), positive and negative ionograms of the original solution of Zuo Gui Wan Tang (Fig. 2C, D), positive and negative ionograms of the serum of control rats (Fig. 2E, F) and positive and negative ionograms of the serum of ZGW-treated rats (Fig. 2G, H).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 UPLC-MS/MS analysis of the active metabolites of ZGW-containing sera\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe precise mass-to-charge ratio (m/z) of the metabolites was obtained by UPLC-MS and the secondary fragmentation ions of this mass were obtained by secondary mass spectrometry. Multidimensional analyses were carried out to confirm the identified metabolites through factors such as precision of the mass of the parent ions, secondary fragmentation matches, isotopic distributions and peak intensities. The TCMSP database (https://tcmsp-e.com/tcmsp.php), HERB database (http://herb.ac.cn), and etcm database (http://www.tcmip.cn/ETCM/index.) were used to establish a database of Zuo Gui Wan compositions, and by comparing with UPLC- MS results to detect 209 chemical metabolites of the Chinese herbal preparation ZGW. It was set that differences in drug-containing serum exceeding five times that of control serum were considered significant, and 20 significant duplicate metabolites were observed in drug-containing serum. Table 3 provides the Major drug-containing serum metabolites of Zuo Gui Wan (ZWG) identified by LC-MS/MS. Detailed fragmentation patterns are provided in Supplementary 1 Table S1.A detailed list of the metabolites identified in the ZGW decoction is presented in \u0026nbsp;Supplementary 2 and \u0026nbsp;Supplementary 3.\u003c/p\u003e\n\u003cp\u003eTable 3. Major drug-containing serum metabolites of Zuo Gui Wan (ZWG) identified by LC-MS/MS.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMetabolite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003em/z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRetention time (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdduct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMass Error (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7-epi-loganin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e229.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8-epiloganic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e375.1306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8-epiloganin deglycosylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e390.1525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+NH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCatalpol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e407.1203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+FA-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDehydromevalonic lactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e205.0818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDl-tyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e213.1231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+NH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFarnesylacetone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e456.2954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+NH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeniposide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e401.1098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMellitoxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e369.0834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+FA-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMethyl (trihydroxy-cyclopentapyran carboxylate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e288.1074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+NH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMethyl 3-hydroxy-1-methyl-hexahydropyran carboxylate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e429.1368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhenylalanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e185.1286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+NH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRehmaionoside a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e380.2617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+NH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSweroside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e403.1256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+FA-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTryptophan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e219.1128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2-amino-2-deoxy-alpha-D-glucopyranose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e184.0427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2-phenylethanol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163.0866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2-phenylpropionic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e344.1339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+NH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3,4-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e200.0554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM+NH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(2s,3r,4r,5s,6r)-2-[[(1s,4as,5r,7ar)-4a,5-dihydroxy-7-(hydroxymethyl)-5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e357.0839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;Notes: Only the major metabolites are listed here. Full fragmentation patterns are provided in Supplementary Table S1.\u003cbr\u003e\u0026nbsp;Retention time (RT) and mass-to-charge ratio (m/z) were obtained using LC-MS/MS. Adducts and mass error (ppm) indicate measurement accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Identification of ZGW metabolites and Target Prediction\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThrough mass spectral analysis of TCM metabolite samples, 209 chemical metabolites were detected. From these, 20 migrated metabolites were pinpointed in the serum samples, based on their in vitro chemical profiles and primary and secondary metabolites. Isomeric and Canonical SMILES for these metabolites were retrieved from PubChem. Target prediction was carried out using the Swiss Target Prediction and Super-PRED databases. Swiss Target Prediction revealed 532 targets with a probability greater than zero, and Super-PRED indicated 1,187 targets with a probability of 60% or higher. After establishing the metabolite-target correlations, a total of 1,648 unique targets were identified, which was reduced to 464 distinct targets upon removing redundant metabolite-target associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 ZGW Improves Bone Loss and Tissue Pathological Damage, and Modulates Serum Bone Metabolic Factors in OVX Rats\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo simulate PMOP, we used the ovariectomized (OVX) method to establish the model. By analyzing femoral micro-CT images of different groups (Fig 3A), we observed that the NC and SHAM groups displayed normal trabecular bone structures, while the OVX group showed significant trabecular atrophy, fragmentation, and irregular distribution, accompanied by an expansion of the marrow cavity. After treatment with different doses of Zuo Gui Wan (ZGW-L, ZGW-M, ZGW-H) and alendronate sodium (ALN), the microstructure of the femur was significantly improved, and the integrity of the trabeculae was restored. Notably, the mid- and high-dose groups showed significant improvements in trabecular spacing (Tb.Sp), trabecular number (Tb.N), and bone volume fraction (BV/TV) (Fig 3B-G).\u003c/p\u003e\n\u003cp\u003eTo further assess the effect of ZGW on trabecular structure, we performed HE staining of the femur. As shown in Fig 4A, the NC and SHAM groups exhibited an orderly trabecular structure with a well-organized marrow cavity, whereas the OVX group showed an enlarged marrow cavity, disordered trabecular structure, and new adipocyte formation. HE staining results also indicated that treatment with mid- and high-dose ZGW or ALN improved the pathological changes in bone tissue induced by ovariectomy.\u003c/p\u003e\n\u003cp\u003eELISA quantitative analysis of serum bone metabolic markers showed that the levels of BLAP, E2, and BGP in the OVX group were significantly lower than those in the NC and SHAM groups, while CTX-I was significantly higher in the OVX group, confirming successful modeling. Compared to the OVX group, the ZGW-H and ALN groups showed significantly increased levels of BLAP, E2, and BGP, indicating the osteogenic effect of ZGW. Additionally, compared to the OVX group, the levels of CTX-I in the ZGW-M, ZGW-H, and ALN groups were significantly reduced, indicating that ZGW significantly inhibited bone resorption (Fig 4B-E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 network analysis Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.1 Prediction of ZGW targets for PMOP treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTargets relevant to PMOP were sourced from multiple databases. Specific counts included 4 from the Therapeutic Target Database (TTD), 78 from DrugBank, 1,118 from GeneCards, 45 from Online Mendelian Inheritance in Man (OMIM), and 171 from DisGeNET. Additionally, the top 1,000 most relevant genes were selected from the GSE230665 dataset. After consolidating and deduplicating these sources, a total of 2,316 targets associated with PMOP were compiled (Fig. 3A).Taking the intersection of the 464 ZGW predicted targets with the 2316 PMOP-associated targets produced 144 cross-targets for ZGW treatment of PMOP (Fig. 3B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.2 Construction of the \u0026quot;Herb-metabolite-Target-Disease\u0026quot; Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis dataset, including 20 active ZGW metabolites known to impact PMOP targets and the drug names, was imported into Cytoscape 3.8.0 to establish the \u0026ldquo;botanical drug-metabolite-Target-Disease\u0026rdquo; network (Fig.5C). Within this network, the centrality of nodes is indicated by node betweenness, where higher values signify greater importance. Rehmaionoside A exhibited the highest node betweenness, recorded at 1488.0874, followed by Farnesylacetone, Tryptophan, and 3,4-Dihydroxybenzoic Acid with betweenness scores of 1209.7041, 804.7451, and 486.71744, respectively. These prominent metabolites may play key roles in ZGW\u0026rsquo;s efficacy against PMOP. Table 4 lists the top 10 active metabolites as ranked by node betweenness.\u003c/p\u003e\n\u003cp\u003eTable 4. The top 10 active metabolites ranked by node betweenness\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecule name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBetweenness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eRehmaionoside a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e1488.0874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eFarnesylacetone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e1209.7041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eTryptophan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e804.7451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e3,4-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e486.71744\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eGeniposide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e413.7369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eDl-tyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e338.42392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eSweroside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e303.73923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003ePhenylalanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e264.03958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eMellitoxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e249.03209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eCatalpol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e229.59859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.3 PPI\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Analysis of Intersecting Targets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA PPI network was built by analyzing the 144 overlapping targets between ZGW and PMOP, which was made possible by the STRING 11.0 database. With 144 nodes and 1494 edges, this network shows how the intersecting targets interact with one another. Node stands for a target and edge for the interactions between them. Cytoscape 3.8.0 was used to visualize the PPI network （Fig.6A）, and the CytoNCA plugin was used to determine the core targets by assessing the centrality of the target set. The ten most central genes, listed by their degree of connectivity (Table 5). A Sankey diagram was created to depict the links between ZGW, these ten core genes, and the five metabolites most closely associated with these core genes （Fig.6B）.\u003c/p\u003e\n\u003cp\u003eTable 5. Top ten core targets of the PPI network for the ZWG and PMOP intersection targets\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eTarget\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eBetweenness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eCloseness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e2725.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.71649486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1680.9844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.6780488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eESR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1000.3308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.640553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e769.21936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.640553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003ePTGS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1053.1273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.6347032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eMMP9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e595.7026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.61777776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eSTAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e456.10052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.6233184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eNFKB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e368.08936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.62053573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eHIF1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e453.73227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.6043478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eTLR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e482.62494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.6096491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.4 GO enrichment and KEGG pathway analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used GO and KEGG enrichment analysis to learn more about the biological processes that make ZGW work to treat PMOP. Important biological processes (BP)（Fig.7A）, cellular metabolites (CC)（Fig.7B）, and molecular functions (MF)（Fig.7C） were identified. Key biological processes included responses to peptides, nutrients, extracellular stimuli, and hypoxia. Changes in cellular metabolites were primarily linked to membrane rafts, membrane microdomains, vesicle lumens, and secretory granule lumens. Functional molecular interactions were mostly linked to nuclear receptor, ligand-activated transcription factor, and serine-type peptidase activities.Fig.5D details the top 25 pathways, out of 158 important signaling pathways (P\u0026lt;0.05) identified by the KEGG pathway enrichment analysis. Important routes that have been found for ZGW to exert its therapeutic benefits on PMOP include the HIF-1 pathway, the estrogen pathway, the PI3K-Akt pathway, and cellular senescence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.5 \u0026nbsp;Molecular Docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular docking studies were performed to evaluate the binding efficiency of four core metabolites\u0026mdash;Dehydromevalonic lactone, 3,4-dihydroxybenzoic acid, 2-phenylethanol, 2-amino-2-deoxy-alpha-D-glucopyranose, and DL-tyrosine\u0026mdash;with ten corresponding hub genes (ALB, EGFR, ESR1, PTGS2, MMP9, STAT3, GAPDH, NFKB1, TLR4, and HIF1A). Binding affinity was analyzed, with higher absolute values indicating stronger binding capabilities (negative values indicate favorable binding). Table 6 summarizes the binding energies between these core metabolites and the hub genes. Specifically, 3,4-dihydroxybenzoic acid demonstrated significant docking effects with MMP9, PTGS2, and EGFR, exhibiting binding energies of -8.7, -7.2, and -7.1 kcal/mol, respectively. DL-tyrosine showed notable binding energies of -7.3 and -6.9 kcal/mol with PTGS2 and HIF1A, respectively. The molecular interactions were visualized in 3D using PyMol 2.4.0. Fig8.A-F depict the interactions between 3,4-dihydroxybenzoic acid and NFKB1, MMP9, HIF1A, and between DL-tyrosine and STAT3, PTGS2, and NFKB1, respectively.\u003c/p\u003e\n\u003cp\u003eTable 6. Table of molecular docking binding energies between core metabolites and corresponding core targets\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 286px;\"\u003e\n \u003cp\u003emetabolite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eTarget\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003erespective binding energies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003eDehydromevalonic lactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003eDehydromevalonic lactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003eDehydromevalonic lactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eESR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e3,4-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003ePTGS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e3,4-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e3,4-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eMMP9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e3,4-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eSTAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e3,4-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e3,4-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eNFKB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e3,4-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eTLR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e3,4-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eHIF1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e2-phenylethanol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eNFKB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e2-phenylethanol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eHIF1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e2-phenylethanol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eTLR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e2-amino-2-deoxy-alpha-d-glucopyranose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eNFKB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e2-amino-2-deoxy-alpha-d-glucopyranose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eHIF1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e2-amino-2-deoxy-alpha-d-glucopyranose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eTLR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003eDl-tyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eHIF1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003eDl-tyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eNFKB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003eDl-tyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003ePTGS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003eDl-tyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eTLR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003eDl-tyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eSTAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e-6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 In vitro experimental validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.1 Safety of Drug-Containing Serum and Its Effect on the Proliferation of MC3T3-E1 Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell viability was assessed by converting OD values using the formula: [(experimental group - blank group) / (control group - blank group)]. This calculation facilitated comparisons of the impact of drug-containing serum on MC3T3-E1 cell proliferation. The group exposed to serum containing 20% of the medication had the highest cell viability 48 hours after treatment (Fig. 11), this group\u0026apos;s viability was much greater than the control serum group\u0026apos;s. Under the same time and serum concentration, the cell viability of the drug containing serum group was generally higher than that of the control serum group. After 48 hours of intervention with drug containing serum at each concentration, the cell viability was higher than that of the control serum group at the corresponding concentration and time (P\u0026lt;0.05). After 48 hours of equal intervention, the 20% drug containing serum group was higher than other concentration groups(P\u0026lt;0.05)，Consequently, a 48-hour exposure at 20% concentration of drug-containing serum was selected for further experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.2 Effect of ZGW-containing serum on alkaline phosphatase activity in MC3T3-E1 cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe absorbance values were converted to alkaline phosphatase activity values (ALP) according to the instructions, and the 20% Zuo Gui Wan-containing serum intervention group could up-regulate the cellular alkaline phosphatase activity to a certain extent compared with the\u0026nbsp;control\u0026nbsp;serum intervention group (P \u0026lt; 0.01).(Fig.7B)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.3 Effect of ZGW-containing serum on osteogenic differentiation of MC3T3-E1 cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlizarin red S staining was performed after 20 d of osteogenic induction, and under 100x microscope, the number of red calcium nodules in the Zuo Gui Wan containing serum intervention group was significantly more than that in the control serum intervention group (Fig.9C-D), and the staining area was statistically analysed by imagej, and the staining area of the 20% Zuo Gui Wan containing serum group with a volume fraction of 20% Zuo Gui Wan was better than that of the 20% control serum group (P \u0026lt; 0.01) (Fig.9E))\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.4 Effect of ZGW drug-containing serum on core target mRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used RT-qPCR to measure mRNA expression of target proteins in MC3T3-E1 cells treated with 20% control rat serum (NC group) and 20% drug-containing serum (ZGW group).Compared with the NC group, the mRNA levels of ALB, EGFR, and MMP9 were statistically significantly higher in the ZGW group, and the mRNA levels of STAT3, NFKB1, TLR4, and HIF-1\u0026alpha; were statistically significantly lower (Fig. 10A).Compared with the NC group, the mRNA of PI3K-AKT-mTOR-related pathway proteins: PI3KR1, PI3KCA, PI3KCB, NOS3 were up-regulated, and MTOR was decreased in the ZGW group, with a statistically significant difference (Fig.10B), and the mRNA of bone-marking proteins: OPG, OSTERIX, RUNX2 were up-regulated in the ZGW group compared with the NC group.The difference was statistically significant (Fig.10C), ZGW may have a therapeutic effect on PMOP by affecting core genes that are essential for osteoblast differentiation and cell proliferation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.5 Effect of ZGW-containing serum on core target proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used WB method to measure the expression of target proteins in MC3T3-E1 cells treated with 20% control rat serum (NC group) and 20% drug-containing serum (ZGW group).Compared with the NC group, the protein levels of ALB and MMP9 were statistically significantly higher in the ZGW group, and the protein levels of EFGR, STAT3, NFKB1, TLR4 and HIF-1\u0026alpha; were statistically significantly lower (Fig. 11A).Based on the quantitative analysis of ZGW-containing serum for relevant proteins(Fig.11B), Compared with the NC group, the ZGW metabolite bone marker proteins: OPG, OSTERIX, and RUNX2 were increased with a statistically significant difference (Fig. 11B), and ZGW may have a therapeutic effect on PMOP by affecting core genes that are essential for osteoblast differentiation and cell proliferation.To further investigate the effects of ZGW on the PI3K and HIF pathways, we used WB methods to measure the expression of relevant proteins in BMSCS cells treated with 20% blank rat serum (NC group) and 20% drug-containing serum (ZGW group) (Fig. 11D, E), and the results showed that ZGW activated the PI3K and HIF pathways in BMSCS cells, and promoted osteogenic protein expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.6 ZGW Exerts Its Effect via the PI3K and HIF Pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on previous experimental results, we used 10 \u0026micro;mol LY294002 and 100 \u0026micro;mol DMOG to intervene in the cells to verify that Zuo Gui Wan (ZGW) promotes osteogenesis by activating the PI3K signaling pathway and HIF signaling pathway. As shown in Fig 12, cells were treated separately with LY294002 and DMOG to verify the effectiveness of the inhibitors. In Fig 12, ZGW was found to elevate the protein levels of Runx2 and OPG, and LY294002 inhibited this effect. Additionally, DMOG treatment partially reversed the inhibitory effect of LY294002, although this reversal was not significant. Further studies are needed to explore the underlying mechanisms.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe symptoms of osteoporosis, a metabolic bone illness that affects the entire body, include a decrease in bone mass, degradation of microarchitecture, and an elevated risk of fractures[\u003csup\u003e16]\u003c/sup\u003e. Globally, it affects approximately 200 million people as per the International Osteoporosis Foundation[\u003csup\u003e17]\u003c/sup\u003e. In China, the prevalence among women over 50 years old is 32.1%, increasing to 51.6% in women over 65. Alternatively, in the US and EU, about 30% of women experience PMOP, which greatly affects the survival rate of older women. Hip fractures are among the most deadly osteoporotic fractures, although they can occur in any bone in the body[\u003csup\u003e18]\u003c/sup\u003e. Current pharmacological interventions primarily include bisphosphonates such as alendronate and risedronate, which suppress osteoclast activity and mitigate bone loss. In contrast to calcitonin, which inhibits osteoclasts to decrease bone resorption, selective estrogen receptor modulators (SERMs) such as raloxifene increase bone density by mimicking estrogen\u0026apos;s effects. Parathyroid hormone analogs such as teriparatide stimulate bone formation, increasing bone density. Additionally, anti-RANKL antibodies like denosumab obstruct RANKL[\u003csup\u003e19]\u003c/sup\u003e to reduce osteoclast formation. Despite their efficacy, these treatments often entail side effects, such as increased risk of gastric ulcers and esophagitis from alendronate or high costs, making them inaccessible in certain regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTCM has long been utilized in treating osteoporosis, with various botanical drugs known to relieve joint pain and fortify bones and muscles. For example, silybin from Silybi Fructus boosts osteoblast proliferation and differentiation, enhances alkaline phosphatase mineralization and upregulates the transcription of osteocalcin, Runx-2, SOD-2, and SIRT1 under iron overload[\u003csup\u003e20]\u003c/sup\u003e. Morinda officinalis oligosaccharides increase OPG mRNA and protein levels while reducing RANKL mRNA and protein expression in bone tissues, influencing the OPG/RANKL/RANK pathway to counteract osteoporotic effects[\u003csup\u003e21]\u003c/sup\u003e. ZGW, based on \u0026quot;Jingyue Quanshu\u0026middot;New Prescriptions Eight Arrays,\u0026quot; is widely used in clinical settings to address osteoporosis[\u003csup\u003e22]\u003c/sup\u003e. It consists of eight metabolites, including Cuscutae Semen, Dioscoreae Rhizoma, Lycii Fructus, Cervi Cornus Colla, Rehmanniae Radix Praeparata, Corni Fructus, Testudinis Carapacis Et Plastri Colla, and Cyathulae Radix, traditionally employed to alleviate symptoms such as lumbar acid, leg softness, and internal deficiency due to yin deficiency. Notably, Cuscutae Semen protects osteoblasts and restricts osteoclastogenesis, mitigating glucocorticoid-induced osteoporosis[\u003csup\u003e23]\u003c/sup\u003e, while new glycosides from Corni Fructus promote osteoblast proliferation and differentiation, regulating genes and proteins involved in osteogenic pathways[\u003csup\u003e24]\u003c/sup\u003e. However, the precise metabolites and mechanisms underlying ZGW\u0026rsquo;s effects on PMOP remain elusive. This study is the first of its kind to employ UPLC-MS/MS, network analysis, and molecular docking to determine the targets and core metabolites of ZGW and to explain how it may operate. In vivo cellular experiments using MMT, ALP, alizarin red S staining, PCR, and WB to verify the osteogenic effect of ZWG.\u003c/p\u003e\n\u003cp\u003eIn this study, we applied UPLC-MS/MS technology to identify 209 metabolites within ZGW decoction and detected 20 additional metabolites in serum samples. Using the Swiss Target Prediction and Super-PRED databases, we predicted 464 targets. Moreover, we retrieved 2,316 PMOP-related targets from the databases DrugBank, GeneCards, TTD, DisGeNET, OMIM, and GEO. The intersection of ZGW and PMOP targets revealed 144 common targets. In our constructed botanical drug-metabolite-target-disease network, certain metabolites such as Rehmaionoside A, Farnesylacetone, Tryptophan, 3,4-dihydroxybenzoic acid, and Geniposide emerged as having numerous targets related to PMOP, suggesting their potential as core metabolites of ZGW. Rehmaionoside A, a terpenoid glycoside found predominantly in Rehmanniae Radix Praeparata, exhibits anti-osteoporotic properties by regulating kidney and liver functions and enhancing blood circulation[\u003csup\u003e25]\u003c/sup\u003e. Farnesylacetone, a terpene ketone, functions as a hormone and metabolite, playing a role in the synthesis of macromolecules in crustacean gonads[\u003csup\u003e26]\u003c/sup\u003e. The amino acid tryptophan influences osteoporosis via regulating the metabolism of SCFAs and TMAO. It is a precursor to the neurotransmitters serotonin, melatonin, and kynurenine[\u003csup\u003e27]\u003c/sup\u003e. Its metabolism via the kynurenine pathway impacts osteoblastogenesis, with certain oxidation products inhibiting the proliferation and differentiation of Bone Marrow Stromal Cells (BMSCs) and osteoblasts[\u003csup\u003e28]\u003c/sup\u003e. 3,4-dihydroxybenzoic acid, positioned hydroxyl groups at the 3rd and 4th locations, serves as an exogenous metabolite and antitumor agent in humans. The chemical has a dual effect during osteogenesis\u0026mdash;it increases intracellular mineralization\u0026mdash;and adipogenesis\u0026mdash;it decreases lipid accumulation in BMSCs and MC3T3-E1 cells[\u003csup\u003e29]\u003c/sup\u003e. It promotes osteogenesis and inhibits adipogenesis, contributing to osteoporosis treatment. In addition to suppressing the expression of osteoclast-specific markers like MMP, c-Src, and the transcription factors AP-1, 3,4-dihydroxybenzoic acid induces osteoclast apoptosis by means of mitochondrial membrane potential alterations, and caspase activation[\u003csup\u003e30]\u003c/sup\u003e. Conversely, geniposide, a terpenoid glycoside present in Rehmanniae Radix Praeparata and other botanical drugs, mitigates endoplasmic reticulum stress and reduces dexamethasone-induced osteoblast apoptosis. It enhances mitochondrial resilience against dexamethasone-induced apoptosis in MC3T3-E1 cells[\u003csup\u003e31]\u003c/sup\u003e by upregulating the NRF2 pathway and downregulating the NF-\u0026kappa;B pathway, while activating the GLP-1R / ABCA1 and ERK signaling pathways[\u003csup\u003e32]\u003c/sup\u003e. This facilitates the alleviation of glucocorticoid-induced osteogenic differentiation inhibition, inhibits c-Fos protein hydrolysis, and prevents I\u0026kappa;B degradation, thereby reducing RANKL-induced osteoclast differentiation and mitigating osteoporosis progression[\u003csup\u003e33]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn this study, we established a PPI network to identify common targets between ZGW and PMOP and evaluated the key gene functions via GO and KEGG pathway analyses. Within the PPI network, the genes ALB, EGFR, NFKB1, TLR4, STAT3, HIF1A, MMP9, and ESR1 exhibited elevated centrality, highlighting their potential as pivotal targets for ZGW\u0026rsquo;s efficacy in PMOP treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eALB (serum albumin) is a key protein in maintaining plasma osmolality and nutrient transport with antioxidant and calcium ion binding functions. Low serum ALB levels are significantly associated with decreased bone mineral density and increased fracture risk[\u003csup\u003e34]\u003c/sup\u003e。ALB promotes osteoblast mineralization by binding calcium ions and IGF-1 and scavenges ROS to protect cells from oxidative damage[\u003csup\u003e35]\u003c/sup\u003e。ALB promotes osteoblast mineralization by binding calcium ions and IGF-1 and scavenges ROS to protect cells from oxidative damage[\u003csup\u003e36]\u003c/sup\u003e.MMP9 (matrix metalloproteinase 9) is a metalloproteinase that degrades the extracellular matrix (ECM) and is involved in bone remodelling and angiogenesis.Enhanced activity of MMP9 is accompanied by collagen degradation in the ECM, releasing osteogenic differentiation-associated growth factors (e.g. TGF-\u0026beta;) that bind to the ECM, thus indirectly activating the BMP/Smad pathway to drive osteoblasts[\u003csup\u003e37]\u003c/sup\u003e，However, long-term high expression inhibits osteogenic differentiation[\u003csup\u003e38]\u003c/sup\u003e.EGFR (epidermal growth factor receptor) EGFR is a tyrosine kinase receptor that regulates cell proliferation and survival and activates the MAPK and PI3K pathways.EGFR signalling enhances negative regulation of mTOR signalling to control the promotion of osteoblast differentiation.[\u003csup\u003e39]\u003c/sup\u003e。At the same time, EGFR inhibitors can suppress bone resorption by inhibiting the process of polarisation of M1 macrophages towards osteoclast differentiation[\u003csup\u003e40]\u003c/sup\u003e，reflecting the bidirectional benefits of EGFR signalling on osteogenesis.STAT3 (Signal Transducer and Activator of Transcription 3) is a transcription factor that plays a central role in a variety of pathological processes by regulating cell proliferation, apoptosis, and inflammatory responses.In osteoporosis, aberrant activation of STAT3 exacerbates the inflammatory microenvironment and stimulates the JAK/NF-\u0026kappa;B pathwayinteraction, promoting osteoclast differentiation and inhibiting osteoblast activity.\u003c/p\u003e\n\u003cp\u003e[\u003csup\u003e41]\u003c/sup\u003e。NFKB1 (Nuclear Factor Kappa B Subunit 1) is a core subunit of the NF-\u0026kappa;B signalling pathway, and aberrant activation of NFKB1 promotes osteoclast differentiation and inhibits osteoblast activity through the up-regulation of pro-inflammatory factors, such as TNF-\u0026alpha; and IL-6, leading to bone resorption-formation imbalance[\u003csup\u003e42]\u003c/sup\u003e，Inhibition of NFKB1 was shown to reduce RANKL expression by blocking the TLR4/MyD88/NF-\u0026kappa;B pathway, thereby inhibiting osteoclastogenesis and alleviating bone loss[\u003csup\u003e43]\u003c/sup\u003e。TLR4 (Toll-Like Receptor 4) is a pattern recognition receptor that mediates natural immune responses and chronic inflammation regulation through activation of the NF-\u0026kappa;B and MAPK pathways.In osteoporosis, overexpression of TLR4 promotes the polarisation of M1-type macrophages, releases a large number of pro-inflammatory factors (e.g., IL-1\u0026beta;, TNF-\u0026alpha;), inhibits osteoblast differentiation and accelerates boneResorption\u003c/p\u003e\n\u003cp\u003e[\u003csup\u003e44]\u003c/sup\u003e。HIF-1alpha (Hypoxia-Inducible Factor 1 Alpha) is a core transcription factor of the hypoxic response and is involved in the maintenance of bone homeostasis by regulating angiogenesis and energy metabolism.HIF-1alpha increases glycolytic responses and promotes osteoclastogenesis[\u003csup\u003e45]\u003c/sup\u003e，Reducing HIF-1\u0026alpha; protein expression reduces oxidative stress in osteoblasts[\u003csup\u003e46]\u003c/sup\u003e。\u003c/p\u003e\n\u003cp\u003eNotably, based on KEGG pathway analysis, the PI3K signalling pathway becomes crucial.The PI3K (phosphatidylinositol 3-kinase) signalling pathway plays an important role in bone homeostasis by regulating cell proliferation, survival, metabolism and differentiation.The pathway consists of catalytic subunits (e.g. PI3KCA, PI3KCB) and regulatory subunits (e.g. PI3KR1), which activate downstream effector molecules, such as AKT/mTOR, by phosphorylating PIP2 to generate PIP3, and the up-regulation of PI3KR1/PI3KCA activates AKT signalling and promotes osteoblast proliferation and differentiation[\u003csup\u003e47]\u003c/sup\u003e，Decreased MTOR activity may promote differentiation of MSCs towards osteogenesis by deregulating its regulation of osteogenic differentiation inhibitors such as PPAR\u0026gamma;[\u003csup\u003e48]\u003c/sup\u003e，NOS3 upregulation may affect bone repair by modulating angiogenesis and inflammatory microenvironment[\u003csup\u003e49]\u003c/sup\u003e。\u003c/p\u003e\n\u003cp\u003eOSTERIX, RUNX2 and OPG are all osteogenic marker proteins, and OSTERIX is a transcription factor with a zinc finger structure that plays an important role in osteoblast differentiation and is an essential gene for osteoblast development[\u003csup\u003e50]\u003c/sup\u003e.RUNX2 is an important transcription factor involved in bone and cartilage development and is an essential gene in osteoblast and chondrocyte differentiation[\u003csup\u003e51]\u003c/sup\u003e.OPG is a lysophosphatidic acid-binding protein belonging to the tumour necrosis factor receptor family, which plays an important role in the regulation of bone remodelling and protects bone tissue by inhibiting osteoclast production and activity[\u003csup\u003e52]\u003c/sup\u003e。\u003c/p\u003e\n\u003cp\u003eTo evaluate the therapeutic effect of ZGW on PMOP, our first step was to measure the safety of ZGW-containing serum, screen the optimal serum-containing concentration, and assess its impact on the proliferation of MC3T3 cells using the MTT method. The results showed that the safety of ZGW-containing serum was good, 20% containing serum was the optimal concentration, and ZGW-containing serum had a statistically significant difference in promoting cell proliferation compared with control serum. Secondly, we detected its effect on the expression of cellular osteogenic marker alkaline phosphatase by ALP kit, and the results showed that the cellular ALP level was significantly increased under the intervention of 20% drug-containing serum. Next, we assessed the effect of ZGW-containing serum on osteogenic mineralisation using alizarin red S staining, and the results showed significant cellular mineralisation under 20% serum-containing intervention. We used quantitative RT-PCR to compare gene expression profiles between control serum control and serum samples containing the ZGW drug. We focused on key genes identified in the PPI network and genes related to the KEGG enrichment pathway in our network pharmacological analysis. Our results showed that mRNA levels of ALB, EGFR, and MMP9 were significantly higher in the ZGW-treated group, while STAT3, NFKB1, TLR4, and HIF-1\u0026alpha; were significantly lower. These changes were consistent with predictions derived from network analysis and molecular docking studies, supporting the role of ZGW in enhancing osteogenesis and its therapeutic application in PMOP management. Further studies of genes related to the PI3K signalling pathway showed that IK3R1, PI3KCA, PI3KCB, and NOS3 were up-regulated and MTOR was decreased in the ZGW group, suggesting that ZGW may promote cellular osteogenic differentiation through activation of the PI3K signalling pathway. We further examined three osteogenic markers to support these findings. The expression levels of OSTERIX, RUNX2, and OPG were significantly elevated in the ZGW group, confirming the ability of ZGW to positively stimulate osteogenesis. This in-depth study supports the promise of ZGW as a treatment for PMOP by elucidating the molecular processes by which ZGW acts. We continued to validate the above gene targets using WB experiments and concluded that under the intervention of 20% drug-containing serum, the protein levels of ALB and MMP9 were statistically significantly elevated in the ZGW group, the protein levels of EFGR, STAT3, NFKB1, TLR4 and HIF-1\u0026alpha; were statistically significantly reduced, suggesting that ZGW may reduce the release of inflammatory factors, improve the bone marrow hypoxic microenvironment, and promote osteoblast differentiation through inhibition of the STAT3/NF-\u0026kappa;B/TLR4/HIF-1\u0026alpha; axis.STAT3 synergistically drives the release of pro-inflammatory factors (TNF-\u0026alpha;, IL-6) in conjunction with NF-\u0026kappa;B signalling, and the activation of TLR4 further amplifies inflammatory cascade responses, and the inhibition of these pathways by ZGW that significantly reducing the level of inflammation in the bone microenvironment. Chronic hypoxia induces bone marrow mesenchymal stem cells (BMSCs) to differentiate into adipocytes through HIF-1\u0026alpha; and activates glycolysis to inhibit osteogenesis.ZGW reduced HIF-1\u0026alpha; expression, reversed hypoxia-driven metabolic reprogramming, and promoted MC3T3-directed differentiation towards the osteogenic lineage.ALB, as a carrier protein, binds and stabilises bone morphogenetic proteins (BMPs) and insulin-like growth factor (IGF-1), and enhances the activity of the PI3K-AKT-mTOR signalling pathway.ZGW may enhance the expression of ALB by up-regulating thePI3K-AKT-mTOR signalling pathway, providing metabolic support to osteoblasts and enhancing bone matrix mineralisation.MMP9 is involved in bone remodelling and angiogenesis, and ZGW may promote bone remodelling and angiogenesis by transiently elevating MMP9 to degrade collagen fibres in the bone matrix and releasing growth factors such as sequestered TGF-\u0026beta; and VEGF.EGFR has a dual benefit on osteogenesis, with early activation of the ERK/PI3K pathway, promoting down-dialled proliferation and RUNX2 expression, but later on, EGFR protein however, the continuous activation of EGFR protein in the later stage will lead to the inhibition of mineralisation. After ZGW intervention, the mRNA level of EGFR increased and the protein level decreased, which may be related to the precise regulation of \u0026quot;transcriptional activation-post-translational inhibition\u0026quot; of EGFR signalling, but the specific mechanism needs to be studied in depth in the future. This demonstrates the multi-target synergy of ZGW in PMOP treatment to promote osteogenic differentiation.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eZGW addresses PMOP through a multifaceted approach involving various metabolites, targets, and metabolic pathways. Our research corroborated ZGW\u0026rsquo;s anti-PMOP effects and delineated its primary metabolites, crucial targets, and underlying mechanisms via methods such as UPLC-MS/MS, network analysis, molecular docking, and experimental protocols. ZGW influences genes including ALB, EGFR, NFKB1, TLR4, STAT3, HIF1A, MMP9, and ESR1, facilitating osteogenesis and curtailing the advancement of PMOP through the modulation of pathways such as PI3K-AKT. This investigation enhances our understanding of PMOP treatment and generates novel avenues for the development of anti-osteoporotic agents. Nevertheless, further studies are required to elucidate the principal active metabolites of ZGW and its more intricate mechanisms in combating PMOP.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe online version contains supplementary material\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimental design and project conception: Yongli Dong, Peng Feng;Experimental implementation: Jinguang Gu, Chenhua Li, Bin Zhang;Experimental data statistics: Weikai Qin, Baoyu Qi; Paper writing: Jinguang Gu. All authors agreed to the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the China Academy of Chinese Medical Sciences (Special Project for Training Outstanding Young Scientific and Technological Talents) and the National Natural Science Foundation of China, under grant number ZZ17-YQ-012 and 82305278.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rat-based research described in this study received ethical approval from the Ethics Center of the Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences. (Approval No. 2025B036), adhering to the guidelines outlined in the EEC Directive of 1986 (86/609/EEC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors state that they have no conflicts of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo;contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of orthopedic surgery, Wangjing Hospital of China Academy of Chinese Medical Sciences,No. 6, Wangjing Zhonghuan South Road, Chaoyang District, Beijing100102, People\u0026rsquo;s Republic of China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArceo-Mendoza R M , Camacho P M .Postmenopausal Osteoporosis: Latest Guidelines[J].Endocrinology and Metabolism Clinics of North America, 2021(2):50.DOI:10.1016/j.ecl.2021.03.009.\u003c/li\u003e\n\u003cli\u003eDennis,M,Black,et al.Postmenopausal Osteoporosis[J].New England Journal of Medicine, 2016.DOI:10.1056/nejmcp1513724.\u003c/li\u003e\n\u003cli\u003eSalari N,Darvishi N,Bartina Y,et al.Global prevalence of osteoporosis among the world older adults:a comprehensivesystematic review and meta-analysis[J].J Orthopaed Surg Res,2021,16(01):669.\u003c/li\u003e\n\u003cli\u003eCompston J , Cooper A , Cooper C ,et al.UK clinical guideline for the prevention and treatment of osteoporosis[J].Archives of Osteoporosis, 2017, 12(1).DOI:10.1007/s11657-017-0324-5.\u003c/li\u003e\n\u003cli\u003eKhosla S , Hofbauer L C .Osteoporosis treatment: Recent developments and ongoing challenges[J].The Lancet Diabetes \u0026amp; Endocrinology, 2017, 5(11).DOI:10.1016/S2213-8587(17)30188-2.\u003c/li\u003e\n\u003cli\u003eXiaohu J, Su G, Yuying Z, Simin C, Wenyan W, Jingjing Y, Meiqiu Y, Jing L, Jie S, Suhong C, Guiyuan L. Traditional Chinese Medicine formula Wubi Shanyao Pills protects against reproductive aging by activating SIRT1/3 to reduce apoptosis. J Ethnopharmacol. 2024 Jan 10;318(Pt B):116976. doi: 10.1016/j.jep.2023.116976. Epub 2023 Jul 29. PMID: 37524234.\u003c/li\u003e\n\u003cli\u003eMeng J, Lv Z, Sun C, Qiao X, Chen C. An extract of Lycium barbarum mimics exercise to improve muscle endurance through increasing type IIa oxidative muscle fibers by activating ERR\u0026gamma;. FASEB J. 2020 Sep;34(9):11460-11473. doi: 10.1096/fj.202000136R. Epub 2020 Jul 30. PMID: 33411401.\u003c/li\u003e\n\u003cli\u003eGao X, Liu Y, An Z, Ni J. Active metabolites and Pharmacological Effects of Cornus officinalis: Literature Review. Front Pharmacol. 2021 Apr 12;12:633447. doi: 10.3389/fphar.2021.633447. PMID: 33912050; PMCID: PMC8072387.\u003c/li\u003e\n\u003cli\u003eLiu F , Tan F , Tong W ,et al.Effect of Zuoguiwan on osteoporosis in ovariectomized rats through RANKL/OPG pathway mediated by \u0026beta;2AR.[J].Biomedicine \u0026amp; Pharmacotherapy, 2018, 103:1052-1060.DOI:10.1016/j.biopha.2018.04.102.\u003c/li\u003e\n\u003cli\u003eYousefzadeh, N., Kashfi, K., Jeddi, S., \u0026amp; Ghasemi, A. (2020). Ovariectomized rat model of osteoporosis: a practical guide. EXCLI journal, 19, 89\u0026ndash;107. https://doi.org/10.17179/excli2019-1990\u003c/li\u003e\n\u003cli\u003eWishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0:a major update to the DrugBank database for 2018. Nucleic Acids Res (2018) 46(D1):D1074\u0026ndash;82. doi: 10.1093/nar/gkx1037\u003c/li\u003e\n\u003cli\u003eMok SRS, Mohan S, Grewal N, Elfant AB, Judge TA. A genetic database can be utilized to identify potential biomarkers for biphenotypic hepatocellular carcinoma-cholangiocarcinoma. J Gastrointest Oncol (2016) 7(4):570\u0026ndash;9. doi: 10.21037/jgo.2016.04.01\u003c/li\u003e\n\u003cli\u003eLi YH, Yu CY, Li XX, Zhang P, Tang J, Yang Q, et al. Therapeutic target databaseupdate 2018: enriched resource for facilitating bench-to-clinicresearch of targeted therapeutics. Nucleic Acids Res (2018) 46(D1):D1121\u0026ndash;7. doi: 10.1093/nar/gkx1076\u003c/li\u003e\n\u003cli\u003ePi\u0026ntilde;ero J, Queralt-Rosinach N, Bravo A, Deu-Pons J, Bauer-Mehren A, Baron M, et al. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (2015) 2015(0):bav028. doi: 10.1093/database/bav028\u003c/li\u003e\n\u003cli\u003eAmberger JS, Bocchini CA, Scott AF, Hamosh A. OMIM.org: leveraging knowledge across phenotype-gene relationships. Nucleic Acids Res. 2019 Jan 8;47(D1):D1038-D1043. doi: 10.1093/nar/gky1151. PMID: 30445645; PMCID: PMC6323937.\u003c/li\u003e\n\u003cli\u003eWalsh LJ, Lewis SA, Wong CA, Cooper S, Oborne J, Cawte SA, Harrison T, Green DJ, Pringle M, Hubbard R, Tattersfield AE. The impact of oral corticosteroid use on bone mineral density and vertebral fracture. Am J Respir Crit Care Med. 2002 Sep 1;166(5):691-5. doi: 10.1164/rccm.2110047. PMID: 12204867.\u003c/li\u003e\n\u003cli\u003eCole ZA, Dennison EM, Cooper C. Osteoporosis epidemiology update. Curr Rheumatol Rep. 2008 Apr;10(2):92-6. doi: 10.1007/s11926-008-0017-6. PMID: 18460262.\u003c/li\u003e\n\u003cli\u003eDewan N , Macdermid J C , Grewal R ,et al.Risk factors predicting subsequent falls and osteoporotic fractures at 4years after distal radius fracture\u0026mdash;a prospective cohort study[J].Archives of Osteoporosis, 2018, 13(1):32.DOI:10.1007/s11657-018-0445-5.\u003c/li\u003e\n\u003cli\u003eStarup-Linde J , Langdahl B , Vestergaard P ,et al.Incident peptic ulcers and concomitant treatment of direct oral anticoagulants and oral bisphosphonates\u0026mdash;a real-world cohort study[J].Osteoporosis International, 2022, 33(6):1323-1334.DOI:10.1007/s00198-022-06315-z.\u003c/li\u003e\n\u003cli\u003eJang S A , Hwang Y H , Kim T ,et al.Water Extract of Agastache rugosa Prevents Ovariectomy-Induced Bone Loss by Inhibiting Osteoclastogenesis[J].Foods, 2020, 9(9):1181.DOI:10.3390/foods9091181.\u003c/li\u003e\n\u003cli\u003eChen Y, Yang C, Deng Z, Xiang T, Ni Q, Xu J, Sun D, Luo F. Gut microbially produced tryptophan metabolite melatonin ameliorates osteoporosis via modulating SCFA and TMAO metabolism. J Pineal Res. 2024 Apr;76(3):e12954. doi: 10.1111/jpi.12954. PMID: 38618998.\u003c/li\u003e\n\u003cli\u003eLiu F, Tan F, Tong W, Fan Q, Ye S, Lu S, Teng Z, Han M, Zhang M, Chai Y. Effect of Zuoguiwan on osteoporosis in ovariectomized rats through RANKL/OPG pathway mediated by \u0026beta;2AR. Biomed Pharmacother. 2018 Jul;103:1052-1060. doi: 10.1016/j.biopha.2018.04.102. Epub 2018 Apr 25. PMID: 29710663.\u003c/li\u003e\n\u003cli\u003eMo H , Zhang N , Li H ,et al.Beneficial effects of Cuscuta chinensis extract on glucocorticoid-induced osteoporosis through modulation of RANKL/OPG signals[J].Brazilian Journal of Medical and Biological Research, 2019, 52(12).DOI:10.1590/1414-431X20198754.\u003c/li\u003e\n\u003cli\u003eHUANG Jia-chun,LIN Yan-ping,CHEN Tong-ying,et al. Effects of cornelianoside Ⅰ on the proliferation and osteogenic differentiation of osteoblasts[J]. Chinese Journal of Osteoporosis,2020,26(01):1-5.\u003c/li\u003e\n\u003cli\u003eLiu C, Ma R, Wang L, Zhu R, Liu H, Guo Y, Zhao B, Zhao S, Tang J, Li Y, Niu J, Fu M, Zhang D, Gao S. Rehmanniae Radix in osteoporosis: A review of traditional Chinese medicinal uses, phytochemistry, pharmacokinetics and pharmacology. J Ethnopharmacol. 2017 Feb 23;198:351-362. doi: 10.1016/j.jep.2017.01.021. Epub 2017 Jan 19. PMID: 28111216.\u003c/li\u003e\n\u003cli\u003eBerreur-Bonnenfant J, Lawrence F. Comparative effect of farnesylacetone on macromolecular synthesis in gonads of crustaceans. Gen Comp Endocrinol. 1984 Jun;54(3):462-8. doi: 10.1016/0016-6480(84)90163-1. PMID: 6203810.\u003c/li\u003e\n\u003cli\u003eChen Y, Yang C, Deng Z, Xiang T, Ni Q, Xu J, Sun D, Luo F. Gut microbially produced tryptophan metabolite melatonin ameliorates osteoporosis via modulating SCFA and TMAO metabolism. J Pineal Res. 2024 Apr;76(3):e12954. doi: 10.1111/jpi.12954. PMID: 38618998.\u003c/li\u003e\n\u003cli\u003eMichalowska M, Znorko B, Kaminski T, Oksztulska-Kolanek E, Pawlak D. New insights into tryptophan and its metabolites in the regulation of bone metabolism. J Physiol Pharmacol. 2015 Dec;66(6):779-91. PMID: 26769827.\u003c/li\u003e\n\u003cli\u003eRivera-Piza A, An YJ, Kim DK, Lee SH, Kim JB, Choi JS, Lee SJ. Protocatechuic Acid Enhances Osteogenesis, but Inhibits Adipogenesis in C3H10T1/2 and 3T3-L1 Cells. J Med Food. 2017 Mar;20(3):309-319. doi: 10.1089/jmf.2016.3833. PMID: 28296593.\u003c/li\u003e\n\u003cli\u003eWu YX, Wu TY, Xu BB, Xu XY, Chen HG, Li XY, Wang G. Protocatechuic acid inhibits osteoclast differentiation and stimulates apoptosis in mature osteoclasts. Biomed Pharmacother. 2016 Aug;82:399-405. doi: 10.1016/j.biopha.2016.05.008. Epub 2016 May 31. PMID: 27470378.\u003c/li\u003e\n\u003cli\u003eXiao Y, Ren Q, Zheng Y, Zhang S, Ouyang J, Jiao L, Tang C, Li L, Shi W, Wang M, Zhang S, Zhang D, Zhong B, Peng F, Chen Z, Wu L. Geniposide ameliorated dexamethasone-induced endoplasmic reticulum stress and mitochondrial apoptosis in osteoblasts. J Ethnopharmacol. 2022 Jun 12;291:115154. doi: 10.1016/j.jep.2022.115154. Epub 2022 Feb 28. PMID: 35240241.\u003c/li\u003e\n\u003cli\u003eXiao Y, Zhang S, Ye Y, Chen J, Xu Y. Geniposide suppressed OX-LDL-induced osteoblast apoptosis by regulating the NRF2/NF-\u0026kappa;B signaling pathway. J Orthop Surg Res. 2023 Aug 30;18(1):641. doi: 10.1186/s13018-023-04125-5. PMID: 37649066; PMCID: PMC10466864.\u003c/li\u003e\n\u003cli\u003eLee CH, Kwak SC, Kim JY, Oh HM, Rho MC, Yoon KH, Yoo WH, Lee MS, Oh J. Genipin inhibits RANKL-induced osteoclast differentiation through proteasome-mediated degradation of c-Fos protein and suppression of NF-\u0026kappa;B activation. J Pharmacol Sci. 2014;124(3):344-53. doi: 10.1254/jphs.13174fp. PMID: 24646621.\u003c/li\u003e\n\u003cli\u003eLi Q, Zhu M, Liu X, Tian C, Li D, Wang H, Liu H. Abnormally low serum albumin levels are associated with abnormal bone mineral density and osteoporotic fractures: a retrospective studies. BMC Musculoskelet Disord. 2024 Nov 7;25(1):888. doi: 10.1186/s12891-024-08021-9. PMID: 39511536; PMCID: PMC11542386.\u003c/li\u003e\n\u003cli\u003eSun Q, Zhang C, Hu G, Zhu K, Zheng S. Albiflorin improves osteoporotic bone regeneration by promoting osteogenesis-angiogenesis coupling of bone marrow mesenchymal stem cells. Biochem Biophys Res Commun. 2025 Mar 25;754:151551. doi: 10.1016/j.bbrc.2025.151551. Epub 2025 Feb 26. PMID: 40022815.\u003c/li\u003e\n\u003cli\u003eLiu M, Zhang H, Li Y, Huang D, Zuo H, Yang J, Chen Z. Loss of MMP9 disturbs cranial suture fusion via suppressing cell proliferation, chondrogenesis and osteogenesis in mice. Matrix Biol. 2024 Dec;134:93-106. doi: 10.1016/j.matbio.2024.10.003. Epub 2024 Oct 5. PMID: 39374863. \u003c/li\u003e\n\u003cli\u003eFernandes CJDC, de Almeida GS, Wood PF, Gomes AM, Bezerra FJ, Vieira JCS, Padilha PM, Zambuzzi WF. Mechanosignaling-related angiocrine factors drive osteoblastic phenotype in response to zirconia. J Trace Elem Med Biol. 2024 Jan;81:127337. doi: 10.1016/j.jtemb.2023.127337. Epub 2023 Nov 4. PMID: 38000168. \u003c/li\u003e\n\u003cli\u003eDa W, Jiang W, Tao L. ROS/MMP-9 mediated CS degradation in BMSC inhibits citric acid metabolism participating in the dual regulation of bone remodelling. Cell Death Discov. 2024 Feb 14;10(1):77. doi: 10.1038/s41420-024-01835-5. PMID: 38355572; PMCID: PMC10866869. \u003c/li\u003e\n\u003cli\u003eLinder M, Hecking M, Glitzner E, Zwerina K, Holcmann M, Bakiri L, Ruocco MG, Tuckermann J, Schett G, Wagner EF, Sibilia M. EGFR controls bone development by negatively regulating mTOR-signaling during osteoblast differentiation. Cell Death Differ. 2018 Jun;25(6):1094-1106. doi: 10.1038/s41418-017-0054-7. Epub 2018 Feb 14. PMID: 29445126; PMCID: PMC5988706. \u003c/li\u003e\n\u003cli\u003eJia Q, Liu L, Yu Y, Wulamu W, Jia L, Liu B, Zheng H, Peng Z, Zhang X, Zhu R. Inhibition of EGFR Pathway Suppresses M1 Macrophage Polarization and Osteoclastogenesis, Mitigating Titanium Particle-Induced Bone Resorption. J Inflamm Res. 2024 Nov 26;17:9725-9742. doi: 10.2147/JIR.S484529. PMID: 39624396; PMCID: PMC11609397.\u003c/li\u003e\n\u003cli\u003eXiu C, Zhang L, Zhang C, Zhang Y, Luo X, Zhang Z, Zhao H, Ji K, Chen Z, He G, Chen J. Pharmacologically targeting fatty acid synthase-mediated de novo lipogenesis alleviates osteolytic bone loss by directly inhibiting osteoclastogenesis through suppression of STAT3 palmitoylation and ROS signaling. Metabolism. 2025 Mar 11;167:156186. doi: 10.1016/j.metabol.2025.156186. Epub ahead of print. PMID: 40081616.\u003c/li\u003e\n\u003cli\u003eYu T, Liu X, Jiang M, Li Y, Su H, Niu B. Cucumber seed polypeptides regulate RANKL-induced osteoclastogenesis through OPG/RANKL/RANK and NF-\u0026kappa;B. In Vitro Cell Dev Biol Anim. 2024 Jan;60(1):54-66. doi: 10.1007/s11626-023-00834-7. Epub 2023 Dec 20. PMID: 38123756; PMCID: PMC10858069. \u003c/li\u003e\n\u003cli\u003eLiu C, Zuo M, Zhao J, Niu T, Hu A, Wang H, Zeng X. DPHB inhibits osteoclastogenesis by suppressing NF-\u0026kappa;B and MAPK signaling and alleviates inflammatory bone destruction. Int Immunopharmacol. 2025 Apr 16;152:114377. doi: 10.1016/j.intimp.2025.114377. Epub 2025 Mar 4. PMID: 40043357. \u003c/li\u003e\n\u003cli\u003eZhang Y, He X, Wang K, Xue Y, Hu S, Jin Y, Zhu G, Shi Q, Rui Y. Irisin alleviates obesity-induced bone loss by inhibiting interleukin 6 expression via TLR4/MyD88/NF-\u0026kappa;B axis in adipocytes. J Adv Res. 2025 Mar;69:343-359. doi: 10.1016/j.jare.2024.04.013. Epub 2024 Apr 16. PMID: 38626873. \u003c/li\u003e\n\u003cli\u003eChu W, Peng W, Lu Y, Liu Y, Li Q, Wang H, Wang L, Zhang B, Liu Z, Han L, Ma H, Yang H, Han C, Lu X. PRMT6 Epigenetically Drives Metabolic Switch from Fatty Acid Oxidation toward Glycolysis and Promotes Osteoclast Differentiation During Osteoporosis. Adv Sci (Weinh). 2024 Oct;11(40):e2403177. doi: 10.1002/advs.202403177. Epub 2024 Aug 9. PMID: 39120025; PMCID: PMC11516099. \u003c/li\u003e\n\u003cli\u003eChen C, Chen B, Lin Y, He Q, Yang J, Xiao J, Pan Z, Li S, Li M, Wang F, Zhang H, Wang X, Zeng J, Chi W, Meng K, Wang H, Chen P. Cardamonin attenuates iron overload-induced osteoblast oxidative stress through the HIF-1\u0026alpha;/ROS pathway. Int Immunopharmacol. 2024 Dec 5;142(Pt A):112893. doi: 10.1016/j.intimp.2024.112893. Epub 2024 Aug 31. PMID: 39217878. \u003c/li\u003e\n\u003cli\u003eHuang G, Yin W, Zhao X, Xu M, Wang P, Li R, Zhou L, Tang W, Jiao J. Osteoking inhibits apoptosis of BMSCs in osteoporotic rats via PI3K/AKT signaling pathway. J Ethnopharmacol. 2025 Jan 31;340:118961. doi: 10.1016/j.jep.2024.118961. Epub 2024 Dec 7. PMID: 39653105. \u003c/li\u003e\n\u003cli\u003eLi H, Gong Y, Wang Y, Sang W, Wang C, Zhang Y, Zhang H, Liu P, Liu M, Sun H. \u0026beta;-Sitosterol modulates osteogenic and adipogenic balance in BMSCs to suppress osteoporosis via regulating mTOR-IMP1-Adipoq axis. Phytomedicine. 2025 Mar;138:156400. doi: 10.1016/j.phymed.2025.156400. Epub 2025 Jan 17. PMID: 39848018.\u003c/li\u003e\n\u003cli\u003eBandara N, Gurusinghe S, Chen H, Chen S, Wang LX, Lim SY, Strappe P. Minicircle DNA-mediated endothelial nitric oxide synthase gene transfer enhances angiogenic responses of bone marrow-derived mesenchymal stem cells. Stem Cell Res Ther. 2016 Apr 1;7:48. doi: 10.1186/s13287-016-0307-2. PMID: 27036881; PMCID: PMC4818467.\u003c/li\u003e\n\u003cli\u003eTakada I, Kouzmenko AP, Kato S. Molecular switching of osteoblastogenesis versus adipogenesis: implications for targeted therapies. Expert Opin Ther Targets. 2009 May;13(5):593-603. doi: 10.1517/14728220902915310. PMID: 19397478.\u003c/li\u003e\n\u003cli\u003eKomori, T., Yagi, H., Nomura, S., et al. (1997). Targeted disruption of Cbfa1 results in a complete lack of bone formation owing to maturational arrest of osteoblasts. Cell, 89(5), 755-764.\u003c/li\u003e\n\u003cli\u003eArafa EA, Elgendy NO, Elhemely MA, Abdelaleem EA, Mohamed WR. Diosmin mitigates dexamethasone-induced osteoporosis in vivo: Role of Runx2, RANKL/OPG, and oxidative stress. Biomed Pharmacother. 2023 May;161:114461. doi: 10.1016/j.biopha.2023.114461. Epub 2023 Mar 6. PMID: 36889109.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-histology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hijo","sideBox":"Learn more about [Journal of Molecular Histology](https://www.springer.com/journal/10735)","snPcode":"10735","submissionUrl":"https://submission.springernature.com/new-submission/10735/3","title":"Journal of Molecular Histology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Network analysis, UPLC-MS/MS, Zuo Gui Wan, Postmenopausal Osteoporosis, Osteoporosis","lastPublishedDoi":"10.21203/rs.3.rs-8011125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8011125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eEthnopharmacological Relevance:\u003c/strong\u003e Zuo Gui Wan (ZGW), a traditional Chinese medicine (TCM) formula, shows potential for treating postmenopausal osteoporosis (PMOP), combining traditional herbal knowledge with modern scientific validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Postmenopausal osteoporosis (PMOP) is a metabolic bone disorder caused by estrogen deficiency, leading to decreased bone mass and increased fracture risk. ZGW has shown promise in managing PMOP, but its active metabolites and mechanisms remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003cbr\u003e\nOvariectomized (OVX) rats were used to model osteoporosis. ZGW's efficacy was evaluated through micro-CT, HE staining, and serum ELISA. Active metabolites in serum were identified by UPLC-MS/MS. A \"botanical drug-metabolite-target-disease\" network was built using network analysis. Pathway enrichment was performed using GO and KEGG in R. Molecular docking of key metabolites and targets was conducted using AutoDock Vina and PyMOL. In vitro assays, including MTT, ALP, Alizarin Red S staining, PCR, and Western blotting, validated osteogenic effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003cbr\u003e\nZGW improved bone microstructure and serum bone metabolism in OVX rats. UPLC-MS/MS identified 209 metabolites, with 20 transferring into the serum. PPI analysis revealed 144 key targets, and molecular docking showed strong binding between active metabolites (e.g., Remycin A, Farnesecin) and their targets, such as ALB and EGFR. GO and KEGG analyses identified pathways like HIF-1, estrogen signaling, and PI3K-Akt. In vitro, ZGW activated these pathways, enhancing osteogenic marker expression and promoting osteoblast proliferation and differentiation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003cbr\u003e\nZGW treats PMOP through multiple mechanisms involving active metabolites, targets, and pathways. It restores normal gene expression and modulates pathways such as HIF-1 and PI3K-Akt, while also inhibiting inflammation. This study highlights the power of combining UPLC-MS/MS with network analysis for exploring TCM formulations in PMOP treatment.\u003c/p\u003e","manuscriptTitle":"Zuo Gui Wan Restores Bone Metabolism in Postmenopausal Osteoporosis through HIF-1 and PI3K–Akt Pathway Modulation: Evidence from UPLC– MS/MS and Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 10:37:57","doi":"10.21203/rs.3.rs-8011125/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-28T17:05:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T03:06:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129852359278514224191190884116517471645","date":"2025-12-21T06:14:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91797927498979867250542665949179976692","date":"2025-12-14T04:48:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T00:29:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209465544516592514558552850278169539470","date":"2025-11-03T16:25:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-03T14:49:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-03T14:46:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-03T09:49:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Molecular Histology","date":"2025-11-02T13:01:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-histology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hijo","sideBox":"Learn more about [Journal of Molecular Histology](https://www.springer.com/journal/10735)","snPcode":"10735","submissionUrl":"https://submission.springernature.com/new-submission/10735/3","title":"Journal of Molecular Histology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a9a81575-e3cc-4c07-b050-47a36f035f61","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2025-12-28T17:08:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 10:37:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8011125","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8011125","identity":"rs-8011125","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}